NIST Special Publication ouO-215

Computer

Systems

Technology

U.S. DEPARTMENT OF

COMMERCE

Technology Administration

National Institute of Standards and

Technology

Nisr

NATL WST. OF STAND t TECH R.I.C.

The Second Text REtrieval

Conference (TREC-2)

D. K. Harman, Editor

NIST

PUBUCATIONS

Alll^ Eflfliss

—QC

100

.U57

#500-215
7he National Institute of Standards and Technology was established in 1988 by Congress to "assist industry in the development of technology . . . needed to improve product quality, to modernize

manufacturing processes, to ensure product reliability . . . and to facilitate rapid commercialization ... of

products based on new scientific discoveries."

NIST, originally founded as the National Bureau of Standards in 1901, works to strengthen U.S.

industry's competitiveness; advance science and engineering; and improve public health, safety, and the

environment. One of the agency's basic functions is to develop, maintain, and retain custody of the national

standards of measurement, and provide the means and methods for comparing standards used in science,

engineering, manufacturing, commerce, industry, and education with the standards adopted or recognized

by the Federal Government.

As an agency of the U.S. Commerce Department's Technology Administration, NIST conducts basic

and applied research in the physical sciences and engineering and performs related services. The Institute does generic and precompetitive work on new and advanced technologies. NIST's research facilities are

located at Gaithersburg, MD 20899, and at Boulder, CO 80303. Major technical operating units and their

principal activities are listed below. For more information contact the Public Inquiries Desk, 301-975-3058.

Technology Services

• Manufacturing Technology Centers Program

• Standards Services

• Technology Commercialization

• Measurement Services

• Technology Evaluation and Assessment

• Information Services

Electronics and Electrical Engineering

Laboratory

• Microelectronics

• Law Enforcement Standards

• Electricity

• Semiconductor Electronics

• Electromagnetic Fields'

• Electromagnetic Technology'

Chemical Science and Technology

Laboratory

• Biotechnology

• Chemical Engineering'

• Chemical Kinetics and Thermodynamics

• Inorganic Analytical Research

• Organic Analytical Research

• Process Measurements

• Surface and Microanalysis Science

• Thermophysics^

Physics Laboratory

• Electron and Optical Physics

• Atomic Physics

• Molecular Physics

• Radiometric Physics

• Quantum Metrology

• Ionizing Radiation

• Time and Frequency'

• Quantum Physics'

Manufacturing Engineering Laboratory

• Precision Engineering

• Automated Production Technology

• Robot Systems

• Factory Automation

• Fabrication Technology

Materials Science and Engineering

Laboratory

• Intelligent Processing of Materials

• Ceramics

• Materials Reliability'

• Polymers

• Metallurgy

• Reactor Radiation

Building and Fire Research Laboratory

• Structures

• Building Materials

• Building Environment

• Fire Science and Engineering

• Fire Measurement and Research

Computer Systems Laboratory

• Information Systems Engineering

• Systems and Software Technology

• Computer Security

• Systems and Network Architecture

• Advanced Systems

Computing and Applied Mathematics

Laboratory

• Applied and Computational Mathematics^

• Statistical Engineering^

• Scientific Computing Environments^

• Computer Services^

• Computer Systems and Communications^

• Information Systems

'At Boulder, CO 80303. ^Some elements at Boulder, CO 80303.
NIST Special Publication 500-215

The Second Text REtrieval

Conference (TREC-2)

D. K. Harman, Editor

Computer Systems Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899

March 1994

U.S. Department of Commerce

Ronald H. Brown, Secretary

Technology Administration

Mary L. Good, Under Secretary for Technology

National Institute of Standards and Ibchnology

Arati Prabhakar, Director
Reports on Computer Systems Technology

The National Institute of Standards and Technology (NIST) has a unique responsibility for computer

systems technology within the Federal government. NIST's Computer Systems Laboratory (CSL) develops standards and guidelines, provides technical assistance, and conducts research for computers and

related telecommunications systems to achieve more effective utilization of Federal information technology resources. CSL's responsibilities include development of technical, management, physical, and administrative standards and guidelines for the cost-effective security and privacy of sensitive unclassified

information processed in Federal computers. CSL assists agencies in developing security plans and in improving computer security awareness training. This Special Publication 500 series reports CSL re- search and guidelines to Federal agencies as well as to organizations in industry, government, and

academia.

National Institute of Standards and Technology Special Publication 500-215

Natl. Inst. Stand. Technol. Spec. Publ. 500-215, 479 pages (March 1994)

CODEN: NSPUE2

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1994

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402
Preface

This report constitutes the proceedings of the second Text REtrieval Conference (TREC-2) held in Gaithersburg, Maryland. August 31-September 2, 1993. The conference was co-sponsored by the National Institute of

Standards and Technology (NIST) and the Advanced Research Projects Agency (ARPA), and was attended by

150 people involved in the 31 participating groups. The conference was the second in an on-going series of

workshops to evaluate new technologies in text retrieval. The workshop included plenary sessions and twelve discussion groups. Because the participants in the workshop drew on their personal experiences, they sometimes cited specific vendors and commercial products.

The inclusion or omission of a particular company or product does not imply either endorsement or criticism by

NIST.

The sponsorship of the Software and Intelligent Systems Technology Office of the Advanced Research Projects Agency is gratefully acknowledged, along with the tremendous work of the program committee.

Donna Haiman

February 20. 1994

TREC-2 Program Cormnittee

Donna Harman. NIST. chair

Chris Buckley. Cornell University

Susan Dumais, Bellcore

Darryl Howard, U.S. Department of Defense

David Lewis, AT & T BeU Labs

John Prange, U.S. Department of Defense

Alan Smeaton. Dublin City University. Ireland Richard Tong, Advanced Decision Systems

Steve Walker. City University. UK

TABLE OF CONTENTS

ABSTRACT viii

PAPERS

1. Overview of the Second Text REtrieval Conference (TREC-2) 1 D. Harman (National Institute of Standards and Technology)

2. Okapi at TREC-2 21

S. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, M. Gatford (City University, London)

3. Combining Evidence for Information Retrieval 35

N. Belkin, P. Kantor, C. Cool, R. Quatrain (Rutgers University)

4. Automatic Routing and Ad-hoc Retrieval Using SMART : TREC 2 45

C. Buckley, J. Allan, G. Salton (Cornell University)

5. Full Text Retrieval based on Probabilistic Equations with Coefficients fitted by

Logistic Regression 57

W. Cooper, A. Chen, F. Gey (University of California, Berkeley)

6. Probabilistic Learning Approaches for Indexing and Retrieval with the TREC-2 Collection .... 67

N. Fuhr, U. Pfeifer, C. Bremkamp, M. PoUmann (University of Dortmund, Germany)

7. TREC-2 Routing and Ad-Hoc Retrieval Evaluation using the INQUERY System 75

W. Croft, J. Callan, J. Broglio (University of Massachusetts, Amherst)

8. DR-LINK: A System Update for TREC-2 85

E. Liddy, S. Myaeng (Syracuse University)

9. Feedback and Mixing Experiments with MatchPlus 101

S. Gallant, W. Caid, J. Carleton, T. Gutschow, R. Hecht-Nielsen, K. Qing, D. Sudbeck (HNC, Inc.)

10. Latent Semantic Indexing (LSI) and TREC-2 105

S. Dumais (Bellcore)

11. An Information Retrieval Test-bed on the CM-5 117

B. Masand, C. StanfiU (Thinking Machines Corporation)

12. Recent Developments in Natural Language Text Retrieval 123

T. Strzalkowski, J. Carballo (New York University)

13. Design and Evaluation of the CLARIT-TREC-2 System 137

D. Evans, R. Lefferts (Carnegie Mellon University and CLARIT Corporation)

14. Bayesian Inference with Node Aggregation for Information Retrieval 151

B. Del Favero, R. Fung (Institute for Decision Systems Research)

V
15. Effective and Efficient Retrieval from Large and Dynamic Document Collections 163

D. Knaus, P. Schauble (Swiss Federal Institute of Technology)

16. N-Gram-Based Text Filtering For TREC-2 .171

W. Cavnar (Environmental Research Institute of Michigan)

17. Retrieval of Partial Documents 181

A. Moffat, R. Sacks-Davis, R. Wilkinson, J. Zobel

(CITRI, Royal Melbourne Institute of Technology)

18. GE in TREC-2: Results of a Boolean Approximation Method for Routing and Retrieval 191

P. Jacobs (GE Research and Development Center)

19. TREC-II Routing Experiments with the TRW/Paracel Fast Data Finder 201

M. Mettler (TRW Systems Development Division)

20. Knowledge-Based Searching with TOPIC 209

J. Lehman, C. Reid (Verity, Inc.)

21. On Expanding Query Vectors with Lexically Related Words 223

E. Voorhees (Siemens Corporate Research, Inc.)

22. TREC-2 Document Retrieval Experiments using PIRCS .233

K. Kwok, L. Grunfeld (Queens College, CUNY)

23. Combination of Multiple Searches 243

E. Fox, J. Shaw (Virginia Tech)

24. Machine Learning for Knowledge-Based Document Routing

(A Report on the TREC-2 Experiment) 253

R. Tong, L. Appelbaum (Advanced Decision Systems)

25. The ConQuest System 265

P. Nelson (ConQuest Software, Inc.)

26. Description of the PRC CEO Algorithm for TREC-2 271

P. Thompson (PRC, Inc.)

27. Report of Progress for TREC-II 275

W, Kelleher (Systems Environments Corporation)

28. UCLA-Okapi at TREC-2: Query Expansion Experiments 279

E. Efthimiadis, P. Biron (University of California at Los Angeles)

29. Incorporating Semantics Within a Connectionist Model and a Vector Processing Model 291

R. Boyd, J. DriscoU (University of Central Florida)

vi
REPORTS OF DISCUSSION GROUPS

1. The Efficiency Issues Workshop Report 303

2. Workshop Report - Use of Training Materials in Constructing Routing Queries 305

APPENDICES

A. TREC-2 Results , A-1

B. System Features B-1

vii
Abstract

This report constitutes the proceedings of the second Text REtrieval Conference (TREC-2) held in Gaithersburg, Maryland, August 31-September 2, 1993. The conference was co-sponsored by the National Institute of

Standards and Technology (NIST) and the Advanced Research Projects Agency (ARPA), and was attended by

150 people involved in the 31 participating groups. The goal of the conference was to bring research groups together to discuss their work on a new large test collection. There was a wide variation of retrieval techniques reported on, including methods using automatic

thesaurii, sophisticated term weighting, natural language techniques, relevance feedback, and advanced pattern

matching. As results had been run through a common evaluation package, groups were able to compare the effectiveness of different techniques, and discuss how differences between the systems affected performance.

The conference included paper sessions and discussion groups. This proceedings includes papers from most

of the participants (several poster groups did not submit papers), along with reports from some of the discussion groups.

viii
Overview of the Second Text REtrieval Conference (TREC-2)

Donna Harman

National Institute of Standards and Technology

Gaithersburg, MD. 20899

1. Introduction

In November of 1992 the first Text REtrieval Conference

(TREC-1) was held at NIST [Harman 1993]. The confer- ence, co-sponsored by ARPA and NIST, brought together

information retrieval researchers to discuss their system

results on a new large test collection (the TIPSTER collection). This was the first time that such groups had ever compared results on the same data using the same evaluation methods and represented a breakthrough in cross- system evaluation in information retrieval. It was also the

first time that most of these groups had used such a large

test collection and therefore required a major effort by all groups to scale up their retrieval techniques.

The overall goal of the TREC initiative is to encourage re- search in information retrieval using large-scale test collections. It is hoped that by providing a very large test collection, and encouraging interaction with other groups

in a friendly evaluation forum, new momentum in infor- mation retrieval wih be generated. Because of the NIST

involvement, groups with commercial retrieval products

have participated in TREC, leading to increased technological transfer between the research labs and the commercial products. TREC has also provided a state-of-theart showcase of retrieval methods for ARPA clients. Whereas the TREC-1 conference demonstrated a wide

range of different approaches to the retrieval of text from

large document collections, the resiilts should be viewed

as very preliminary. Not only were the deadlines for re- sults very tight, but the huge increase in the size of the document collection required significant system rebuild- ing by most groups. Much of this work was a system en- gineering task: finding reasonable data structures to use, getting indexing routines to be efficient enough to index

all the data, finding enough storage to handle the large in- verted files and other structures, etc. Still, the results showed that the systems did the task well, and that auto- matic construction of queries from the topics did as well

as, or better than, manual construction of queries.

The second TREC conference (TREC-2) occurred m August of 1993, less than 10 months after the first confer- ence. In addition to 22 of the TREC-1 groups, nine new

groups took part, brmging the total number of participating groups to 31. Many of the original TREC-1 groups were able to "complete" their system rebuilding and tuning, and in general the TREC-2 results show significant

improvements over the TREC-1 results.

This paper provides an overview of the TREC-2 confer- ence, including a review of the TREC task, a brief description of the test collection being used, and an

overview of the results. The papers from the individual groups should be referred to for more details on specific

system approaches.

2. The TREC Task

2.1 Introduction

TREC is designed to encourage research in information

retrieval using large data collections. Two types of retrieval are being examined ~ retrieval using an "adhoc"

query such as a researcher might use in a Hbrary environment, and retrieval using a "routing" query such as a profile to filter some incoming document stream. The TREC

task is not tied to any given application, and is not primarily concerned with interfaces or optimized response time

for searching. However it is helpful to have some potential user in mind when designing or testing a retrieval sys- tem. The model for a user in TREC is a dedicated

searcher, not a novice searcher, and the model for the ap- pUcation is one needing monitoring of data streams for in- formation on specific topics (routing), and the ability to do adhoc searches on archived data for new topics. It should be assumed that the users need the ability to do

both high precision and high recall searches, and are will- ing to look at many documents and repeatedly modify

queries in order to get high recall. Obviously they would

like a system that makes this as easy as possible, but this ease should be reflected in TREC as added intelligence in the system rather than as special interfaces.

Since TREC has been designed to evaluate system perfor- mance both in a routing (filtering or profiling) mode, and

in an adhoc mode, both functions need to be tested.

1
Training Documents

(Disks 1 and 2)

Test Documents

(Disk 3)

Figure 1. The TREC Task.

The test design was based on traditiaial information

retrieval models, and evaluation used traditional recall and

precision measures. The above diagram of the test design

shows the various components of TEIEC (fig. 1).

This diagram reflects the four data sets (2 sets of topics

and 2 sets of documents) that were provided to participants. These data sets (along with a set of sample rele- vance judgments for the 100 training topics) were used to construct three sets of queries. Ql is the set of queries

(probably multiple sets) created to help in adjusting a sys- tem to this task, to create better weighting algorithms, and

in general to train the system for testing. The results of

this research were used to create Q2, the routing queries

to be used against the test documents. Q3 is the set of

queries created from the test topics as adhoc queries for searching against the training documents. The results from searches using Q2 and Q3 were the official test results sent to NIST.

2.1 Specific Task Guidelines

Because the TREC participants used a wide variety of

indexing/knowledge base building techniques, and a wide

variety of approaches to generate search queries, it was

important to establish clear guidelines for the evaluation

task. The guidelines deal with the methods of indexing/knowledge base construction, and with the methods of

generating the queries from the supplied topics. In general, they were constructed to reflect an actual operational

environment, and to aUow as fair as possible a separation

among the diverse query construction approaches.

There were guidelines for constructing and manipulating

the system data structures. These structures were defined

to consist of the original documents, any new structures

built automaticaUy from the documents (such as inverted

files, thesauri, conceptual networks, etc.), and any new

structures built manually from the documents (such as

thesauri, synonym lists, knowledge bases, rules, etc.). The following guidelines were developed for the TREC

task.

1. System data structures should be built using the

initial training set (documents from disks 1 and 2, training topics 1-100, and the relevance judgments). They may be modified based on the test documents from disk 3, but not based on the test topics.

2. There are parts of the test collection, such as the Wall Street Journal and the Ziff material, that contain manuaUy assigned controlled or uncontrolled

index terms. These fields are delimited by SGML

tags, as specified in the documentation files included with the data. Since the primary focus is on retrieval and routing of naturally occurring text, these manually indexed terms should not be used.

3. Special care should be used in handling the rout- ing task. In a true routing situation, a single document would be indexed and compared against the routing topics. Since the test docxmients are generally indexed as a complete set, routing should be

simulated by not using any information based on

the fuU set of test documents (such as weighting

based on the test collection, total frequency based on the test collection, etc.) in the searching. It is permissible to use training-set collection information however.

Additionally there were guidelines for constructing the queries from the provided topics. These guideUnes were

considered of great importance for fair system comparison and were therefore carefully constructed. Three

generic categories were defined, based on the amount and

kind of manual intervention used.

1. AUTOMATIC (completely automatic initial query

construction)

adhoc queries - The system will automatically

extract information from the topic to construct the query. The query will then be submitted to the sys- tem (with no manual modifications) and the results from the system will be the results submitted to NIST. There should be no manual intervention

that would affect the results.

routing queries ~ The queries should be

2
constructed automatically using the training topics, the training relevance judgments and the train- ing documents. The queries should then be sub- mitted to NIST before the test documents are released and should not be modified after that point. The unmodified queries should be run

against the test docimients and the results submitted to NIST.

2. MANUAL (manual initial query construction)

adhoc queries ~ The query is constructed in some

maimer from the topic, either manually or using

machine assistance. Once the query has been constructed, it will be submitted to the system (with no manual intervention), and die results from die system will be the results submitted to NIST.

There should be no manual intervention after Loitial query construction that would affect the

results. (Manual intervention is covered by the cat- egory labelled FEEDBACK.)

routing queries ~ The queries should be constructed in the same manner as the adhoc queries for MANUAL, but using the training topics, rele- vance judgments, and training documents. They

should then be submitted to NIST before the test documents are released and should not be modified after diat point. The unmodified queries

should be run against the test documents and the

results submitted to NIST.

3. FEEDBACK (automatic or manual query construction with feedback)

adhoc queries ~ The initial query can be constructed using either AUTOMATIC or MANUAL

methods. The query is submitted to the system,

and a subset of the retrieved documents is used for manual feedback, i.e., a human makes judgments

about the relevance of the docvunents in this subset. These judgments may be communicated to the system, which may automatically modify die query, or the human may simply choose to modify

the query himself. At some point, feedback

should end, and the query should be accepted as

final. Systems that submit runs using this method

must submit several different sets of results to allow tracking of the time/cost benefit of doing

relevance feedback.

routing queries — FEEDBACK cannot be used for routing queries as routing systems have not sup- ported feedback.

22 The Participants

There were 31 participating systems in TREC-2, using a wide range of retrieval techniques. The participants were

able to choose from three levels of participation: Cate- gory A, full participation. Category B, full participation

using a reduced dataset (1/4 of the full document set), and

Category C for evaluation only (to allow commercial sys- tems to protect proprietary algorithms). The program

committee selected only 20 category A and B groups to present talks because of limited conference time, and

requested that the rest of the groups present posters. All groups were asked to submit papers for the proceedings.

Each group was provided die data and asked to turn in

either one or two sets of results for each topic. When two

sets of results were sent, they could be made using differ- ent methods of creating queries (AUTOMAIIC, MANUAL, or FEEDBACK), or by using different parameter

settings for one query creation method. Groups could

choose to do die routing task, the adhoc task, or both, and were requested to submit die top 1000 documents

retrieved for each topic for evaluation.

3. The Test Collection

3.1 Introduction

The creation of the test collection (called the TIPSTER

collection) was critical to die success of TREC. Like

most traditional retrieval collections, there are three distinct parts to this collection — the documents, the queries or topics, and the relevance judgments or "right answers." These test collection components are discussed briefly in the rest of this section. For a more complete description

of the collection, see [Harman 1994].

32 The Documents

The documents needed to mirror the different types of

documents used in die dieoretical TREC appUcation.

Specifically they had to have a varied length, a varied

writing style, a varied level of editing and a varied vocabulary. As a final requirement, die documents had to cover

difrierent timeframes to show the effects of document date on the routing task. The documents were distributed as CD-ROMs witii about

1 gigabyte of data each, compressed to fit. The following

shows die actual contents of each disk.

Diskl

. WSJ -Wall Street Journal (1981, 1988. 1989)

. AP - AP Newswire (1989)

3
• ZIFF -- Articles from Computer Select disks (Ziff- Davis Publishing)

• FR -- Federal Register (1989)

• DOE -- Short abstracts from DOE publications

Disk 2

. V^SZ -Wall Street Journal {1990, 1991, 1992)

. AP AP Newswire (1988)

• ZIFF - Articles from Computer Select disks (ZiffDavis Publishing)

• FR ~ Federal Register (1988)

Disks

• SJMN " San Jose Mercury News (1991)

' AP-AP Newswire (1990)

• ZIFF ~ Articles from Computer Select disks (Ziff- Davis Publishing)

. PAT ~ U.S. Patents (1993)

The documents are uniformly formatted into an SGMLlike structure, as can be seen in the following example.

<DOC>

<DOCNO> WSJ880406-0090 <IDOCNO>

<HL> AT&T Unveils Services to Upgrade Phone Net- works Under Global Plan <IHL>

<AUTHOR> Janet Guyon (WSJ Staff) </AUTHOR>

<DATEUNE> NEW YORK <IDArEUNE>

<TEXT>

American Telephone &. Telegraph Co. introduced the

first of a new generation of phone services with broad

implications for computer and communications equipment markets.

AT&T said it is the first national long-distance carrier to announce prices for specific services under a

world-wide standardization plan to upgrade phone net- works. By announcing commercial services under the plan, which the industry calls the Integrated Services

Digital Network, AT&T will influence evolving communications standards to its advantage, consultants said,

just as International Business Machines Corp. has cre- ated de facto computer standards favoring its products.

</TEXT>

<iDOC>

All documents have beginning and end markers, and a imique DOCNO id field. Additionally other fields taken

from the initial data appear, but these vary widely across

the different sources. The documents have differing

amoimts of errors, which were not checked or corrected. Not only would this have been an impossible task, but the

errors in the data provide a better simulation of the TREC

task. Errors in missing document separators or bad document numbers were screened out, although a few were

missed and later reported as errors.

Table 1 shows some basic document collection statistics. Note that although the collection sizes are roughly equivalent in megabytes, there is a range of document lengths

from very short dociraients (DOE) to very long (FR).

Also the range of document lengths within a collection

varies. For example, the documents from AP are similar

in length (the median and the average length are very

close), but the WSJ and ZIFF documents have a wider

range of lengths. The documents from the Federal Register (FR) have a very wide range of lengths.

3.3 The Topics

In designing the TREC task, there was a conscious deci- sion made to provide "user need" statements rather than more traditional queries. Two major issues were involved

in this decision. First there was a desire to allow a wide

range of query construction methods by keeping the topic

(the need statement) distmct from the query (the actual

text submitted to the system). The second issue was the abihty to increase the amount of information available about each topic, in particular to include with each topic a

clear statement of what criteria make a document relevant. The topics were designed to munic a real user's need, and

were written by people who are actual users of a retrieval system. Although the subject domain of the topics was

diverse, some consideration was given to the documents

to be searched. The topics were constructed by doing trial retrievals against a sample of the document set, and then

those topics that had roughly 25 to 100 hits in that sample were used. This created a range of broader and narrower

topics.

The following is one of the topics used in TREC.

<top>

<head> Tipster Topic Description

<nwn> Number: 066

<dom> Domain: Science and Technology

<title> Topic: Natural Language Processing

<desc> Description:

Document will identify a type of natural language processing technology which is being developed or marketed in the U.S.

4

\
Table 1. Document Statistics

OUUSCl UI CUilCCUUU WST <'Hkk<! 1 flnH 91 AP 7TFP X .III MTISl V,UIoKo X OIlU STMN CHisk '^^ PAT CHkW

Size of collection ^megauyics^

951 958 i9n

2SS 188 91

1

31S 248 ^58 951

Number of records

84 9^0 7S/ J, ISO low 96 907 996 087

("disk 2) 74,520 79,923 56,920 20.108

("disk ^1 90 257 78 325 161,021 6 711

Median number of terms per record

JJ J 181 89

(disk 2) 218 346 167 315

(disk 3) 279 358 119 2896

Average number of terms per record

(diskl) 329 375 412 1017 89

(disk 2) 377 370 394 1073

(disk 3) 337 379 263 3543

<smry> Summary:

Document will identify a type of natural language pro- cessing technology which is being developed or marketed in the U.S.

<narr> Narrative:

A relevant document will identify a company or institu- tion developing or marketing a natural language processing technology, identify the technology, and identify one or morefeatures of the company's product.

<con> Concept(s):

1 . natural language processing

2. translation, language, dictionary, font

3. software applications

<fac> Factor(s): <nat> Nationality: U.S. </fac>

<def> Definition(s):

</top>

Each topic is formatted in the same standard method to allow easier automatic construction of queries. Besides a beginning and an end marker, each topic has a number, a short title, a one-sentence description, and a summary

sentence or two that can be used as a surrogate for the full topic (often very similar to the one-sentence description).

There is a narrative section which is aimed at providing a complete description of document relevance for the

assessors. Each topic also has a concepts section with a

list of assorted concepts related to the topic. This section

is designed to provide a mini-knowledge base about a

topic such as a real searcher might possess. Additionally

each topic can have a definitions section and/or a factors

section. The definition section has one or two of the defi- nitions critical to a human understanding of the topic.

The factors section is included to allow easier automatic query building by listing specific items fi-om the narrative

that constrain the documents that are relevant. Two particular factors were used m the TREC-2 topics: a time

factor (current, before a given date, etc.) and a nationality factor (either involving only certain countries or excluding

certain countries).

While the TREC topics did not present a problem in scal- ing, the challenge of either automatically constructing a

query, or manually constructing a query with little fore- knowledge of its searching capability, was a major chal- lenge for TREC participants. In addition to filtering the

relatively large amount of information provided in the

topics into queries, the sometimes narrow definition of

relevance as stated in the narrative was difficult for most

systems to handle.

3.4 The Relevance Judgments

The relevance judgments are of critical importance to a

test collection. For each topic it is necessary to compile a

list of relevant documents; hopefully as comprehensive a

list as possible. For the TREC task, three possible

5
methods for finding the relevant documents could have

been used. In the first method, full relevance judgments

could have been made on over one million documents, for each topic, resulting in over 100 million judgments. This was clearly impossible. As a second approach, a random

sample of the documents could have been taken, with rel- evance judgments done on that sample only. The problem

with this approach is that a random sample tiiat is large

enough to find on the order of 200 relevant docxmients per

topic is a very large random sample, and is likely to result in insufficient relevance judgments. The third method, the one used in TREC, was to make relevance judgments on

the sample of documents selected by the various participating systems. This method is known as die pooling

method, and has been used successfully in creating odier

collections [Sparck Jones & van Rijsbergen 1975]. The

sample was constructed by taking the top 100 documents

retrieved by each system for a given topic and merging

them into a pool for relevance assessment. This is a valid sampling method since all the systems used ranked

retrieval mediods, with those docmnents most likely to be

relevant returned first.

Pooling proved to be an effective method. There was little overlap among the 3 1 systems in their retrieved documents, although considerably more overlap than in TREC-1.

Table 2. Overlap of Submitted Restilts

TREC-2 TREC-1

Max Actual Max Actual

Unique

Documents

Per Topic

(Adhoc, 40 runs 23 groups)

4000 1106.0 3300 1278.86

Unique

Documents

Per Topic

(Routing. 40 runs 24 groups)

4000 1465.6 2200 1066.86

Table 2 shows the overlap statistics. The first overlap

statistics are for the adhoc topics (test topics against train- ing documents disks 1 and 2), and the second statistics are for the routing topics (training topics against test documents disk 3 only). For example, out of a maximum of

4000 possible unique documents (40 runs times 100 documents), over one-fourth of the documents were actually

imique. This means that die different systems were find- ing different documents as likely relevant documents for a

topic. Whereas this might be expected (and indeed has

been shown to occur, Katzer et. al. 1982) firom widely

differing systems, these overlaps were often between two

runs for the same system. One reason for the lack of

overlap is the very large number of documents that contain many of the same terms as the relevant documents,

but the major reason is the very different sets of terms in the constructed queries. This lack of overlap should

improve the coverage of the relevance set. and verifies the use of die pooUng methodology to produce the sample.

The merged list of results was then shown to die human

assessors. Each topic was judged by a single assessor to insure the best consistency ofjudgment. Varying numbers

of documents were judged relevant to the topics. For the TREC-2 adhoc topics (topics 101-150), die median number of relevant documents per topic is 201, down from

277 for topics 51-100 (as used for adhoc topics in TREC-1). Only 11 topics have more than 300 relevant documents, with only 2 topics having more than 500 rele- vant documents. These topics were deliberately made

narrower than topics 51-100 because of a concern that topics witii more dian 300 relevant documents are likely to have incomplete relevance assessments.

4. Evaluation

An important element of TEiEC was to provide a common

evaluation forum. Standard recall^Jrecision and

recall/fallout figures were calculated for each TREC sys- tem and diese are presented in Appendix A. A chart with

additional data about each system is shown in Appendix

B. This chart consolidates information provided by the systems that describe features and system timing, and

allows some primitive comparison of the amount of effort needed to prodtice the results.

4.1 Definition of Recall/Precision and Recall/Fallout

Curves

Figure 2 shows typical recall^recision curves. The x axis

plots the recall values at fixed levels of recall, where

Recall =

number of relevant items retrieved

total number of relevant items in collection

The y axis plots the average precision values at those given recall values, where precision is calculated by

Precision =

number of relevant items retrieved

total number of items retrieved

These curves represent averages over die 50 topics. The

averaging mediod was developed many years ago [Salton

& McGill 1983] and is well accepted by die information

retrieval community. The curves show system perfor- mance across die full range of retrieval, i.e.. at the early

stage of retrieval where the highly-ranked documents give

6
high accoiracy or precisian, and at the final stage of

retrieval where there is usually a low accuracy, but more

complete retrieval. Note that the use of these curves assumes a ranked output from a system. Systems that provide an unranked set of documents are known to be

less effective and therefore were not tested in the TREC

program. The curves in figure 2 show that system A has a much

higher precision at the low recall end of the graph and

therefore is more accurate. System B however has higher

precision at the high recall end of the curve and therefore

will give a more complete set of relevant documents,

assuming that the user is willing to look further in the ranked list. A second set of curves was calculated using the

recall/fallout measures, where recall is defined as before and fallout is defined as

number of nonrelevant items retrieved

total number of nonrelevant items in collection Note that recall has the same definition as the probability

of detection and that faUout has the same definition as the probability of false alarm, so that the recall/fallout curves

are also the ROC (Relative Operating Characteristic) curves used in signal processing. A sample set of curves corresponding to the recall/precision curves is shown m

figure 3. These curves show the same order of perfor- mance as do the recall/precision curves and are provided

as an alternative method of viewing the results. The pre- sent version of the curves is experimental as the curve cre- ation is particularly sensitive to scaling (what range is used for calculating fallout). The high precision section of the curves does not show well in figure 3; the high

recall area dominates the curves. Whereas the recall/precision curves show the retrieval system results as they might be seen by a user (since pre- cision measures the accuracy of each retrieved document

as it is retrieved), the recall/fallout curves emphasize the abiUty of these systems to screen out non-relevant material. In particular the fallout measure shows the discrimation powers of these systems on a large document collection. For example, system A has a fallout of 0.02 at a

recall of about 0.48; this means that this system has

found almost 50% of the relevant documents, while only

retrieving 2% of the non-relevant docxmients.

42 Single-Value Evaluation Measures

In addition to recall/precision and recall/fallout cmves,

there were 2 single-value measures used m TREC-2.

The first measure, the non-interpolated average precision,

corresponds to the area under an ideal (non-interpolated)

recall/precision curve. To compute this average, a

precision average for each topic is first calculated. This is done by computing the precision after every retrieved rel- evant document and then averaging these precisions over

the total number of retrieved relevant documents for that

topic. These topic averages are then combined (averaged)

across all topics in the appropriate set to create the noninterpolated average precision for that set. . The second measiire used is an average of the precision

for each topic after 100 documents have been retrieved for

that topic. This measure is useful because it reflects a

clearly comprehended retrieval point. It took on added

importance in the TREC environment because only the top 100 documents retrieved for each topic were actually assessed. For this reason it produces a guaranteed evaluation point for each system.

4.3 Problems with Evaluation

Since this was the first time that such a large collection of

text has been used in open system evaluation, there were some problems with the existing methods of evaluation. The major problem concerned a thresholding effect caused by the inability to evaluate ALL documents

retrieved by a given system.

For TREC-1 the groups were asked to send in only the top

200 documents retrieved by their systems. This artificial document cutoff is relatively low and systems did not

retrieve all the relevant documents for most topics within

the cutoff. All documents retrieved beyond the 200 mark

were considered nonrelevant by default and therefore the

recall/precision curves became inacciu-ate after about 40%

recall on average. TREC-2 used the top 1000 documents

for evaluation. Figure 4 shows the difference in the

cxirves produced by various evaluation thresholds, includ- ing a curve for no threshold (similar to the way evaluation

has been done on the smaller collections.). These curves show that the use of a 1000-document cutoff has solved

most of the thresholding problem.

Two more issues in evaluation have become important.

The first issue involves the need for more statistical evaluation. As will be seen in the results, the recall/precision curves are often close, and there is a need to check if there

is truly any statistically significant differences between

two systems' results or two sets of results from the same

system. This problem is currently under investigation in collaboration with statistical groups experienced in the evaluation of information retrieval systems.

Another issue mvolves getting beyond the averages to bet- i ter understand system performance. Because of the huge I number of dociunents and the long topics, it is very

7
Sample Recall/Precision Curves

1.00 I

Sample Recall/Fallout Curves

0.75 I

0 35 I—I 1 I I I I I I I I -i— 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

Fallout

. System A System B

Figure 2. A Sample Recall/ftecision Curve. Figure 3. A Sample Recall/Fallout Curve.

8
1.00

Effects of Cutoff on Evaluation

0.80 -

ao

0.60

o

0.40

0.20

0.00

0.00 0.20 0.40 0.60

Recall

0.80 1.00

at 200 at 500 _^ at 1000-^ fiill

Figure 4. Effect of evaluation cutoffs on recall/precision curves.

difficult to perform failure analysis on die results to better understand the retrieval processes being tested. Without

better understanding of underlying system performance, it will be hard to consolidate research progress. Some pre- liminary analysis of per topic performance is provided in section 6, and and more attention will be given to this problem in the future.

5. Results

5.1 Introduction

In general the TREC-2 results showed significant

improvements over the TREC-1 results. Many of the

original TREC-1 groups were able to "complete" their system rebuilding and tuning tasks. The results for TREC-2 therefore can be viewed as the "best first-pass" that most groups can accompUsh on this large amoimt of

data. The adhoc results in particular represent baseline

results from the scalmg-up of current algorithms to large

test collections. The better systems produced similar

resiilts, results diat are comparable to those seen using

these algorithms on smaller test collections. The routing results showed even more improvement over TREC-1 routmg results. Some of this improvement was

due to the availabiUty of large numbers of accurate

relevance judgments for training (unlike TREC-1). but most of the improvements came from new research by

participating groups into better ways of using the training

data.

For full descriptions of each system discussed in this section, see the individual papers in this proceedings.

5.2 Adhoc Results

The adhoc evaluation used new topics (101-150) against

the two disks of training documents (disks 1 and 2). There were 44 sets of results for adhoc evaluation in TREC-2, with 32 of them based on runs for the full data

set. Of these, 23 used automatic construction of queries,

9 used manual construction, and 2 used feedback.

Figure 5 shows the recall/precision curves for the six TREC-2 groups with the highest non-interpolated average

precision using automatic construction of queries. The

results marked "INQOOl" are the INQUERY system from

the University of Massachusetts (see Croft, Callan &

Broglio paper). This system uses probabiUstic term

weighting and a probabilistic inference net to combine

various topic and document features. The resxilts marked

"dortQ2", "Brkly3" and "cmlL2" are all based on the use

of the Cornell SMART system, but with important variations. The "cmlL2" run is the basic SMART system from

9
Cornell University (see Buckley, Allan & Salton paper),

but using less than optimal term weightings (by mistake).

The "dortQ2" results from the University of Dortmund come from using polynomial regression on the training data to find weights for various pre-set term features (see

Fuhr, Pfeifer, Bremkamp, Polhnann & Buckley paper).

The "Brkly3" results from the University of California at Berkeley come from performing logistic regression analysis to learn optimal weighting for various term frequency measures (see Cooper, Chen & Gey paper). The

"CLARTA" system from the CLARTT Corporation

expands each topic with noun phrases found in a the- saiuus that is automatically generated for each topic (see

Evans & Lefferts paper). The "Isiasm" results are from

Bellcore (see Dumais paper). This group uses latent semantic indexing to create much larger vectors than the more traditional vector-space models such as SMART.

The run marked "Isiasm" represents only the base

SMART pre-processing results, however. Due to process- ing errors the "improved" LSI run produced unexpectedly

poor results.

Figure 6 shows the recall/precision curve for the six TREC-2 groups with the highest non-interpolated average

precision using manual construction of queries. It should

be noted that varying amounts of manual intervention were used. The results marked "INQ002", "siems2", and

"CLARTM" are automatically generated queries with

manual modifications. The "INQ(X)2" results reflect vari- ous manual modifications made to the "INQOOl" queries,

with those modifications guided by strict rules. The

"siems2" results from Siemens Corporate Research, Inc. (see Voorhees paper) are based on the use of the Comell

SMART system, but with the topics manually modified

(the "not" phases removed). These results were meant to be the base run for improvements usmg WordNet, but the improvements did not materiaUze. The "CLARTM"

results represent manual weighting of the query terms, as opposed to the automatic weighting of the terms that was

used in "CLARTA." The results marked "Vtcms2",

"CnQst2", and "T0PIC2" are produced from queries constructed completely manually. The "Vtcms2" results are from Virginia Tech (see Fox & Shaw paper) and show the

effects of combining the results from SMART vector- space queries with the results from manually-constructed

soft Boolean P-Norm type queries. The "CnQst2" results, from ConQuest Software (see Nelson paper), use a very

large general-purpose semantic net to aid in constructing

better queries from the topics, along with sophisticated

morphological analysis of the topics. The results marked

•T0PIC2" are fi-om the TOPIC system by Verity Corp.

(see Lehman paper) and reflect the use of an expert sys- tem working off specially-constructed knowledge bases to improve performance.

Several comments can be made with respect to these adhoc results. First, the better results (most of the auto- matic results and the three top manual results) are very

similar and it is unlikely that there is any statistical differ- ences between them. There is clearly no "best" method,

and the fact that these systems have very different approaches to retrieval, including different term weighting

schemes, different query construction methods, and differ- ent similarity match methods imphes that there is much

more to be learned about effective retrieval techniques.

As will be seen in section 6, whereas the averages for the systems may be similar, the systems do better on different topics and retrieve different subsets of the relevant documents. A second point that should be made is that the automatic query construction methods continue to perform as weU

as the manual construction methods. Two groups (the

INQUERY system and the CLARTT system) did explicit

comparision of manually-modified queries vs those that were not modified and concluded that manual modification provided no benefits. The three sets of results based on completely manuaUy-generated queries had even

poorer performance than the manually-modified queries.

Note that this result is specific to the very rich TREC topics; it is not clear that this will hold for the short topics

normally seen in other retrieval environments.

As a final point, it should be noted that these adhoc results represent significant improvements over the results from

TREC-1. Figure 7 shows a comparison of results for a

typical system in TREC-1 and TREC-2. Some of this improvement is due to unproved evaluation, but the differ- ence between the curve marked "TREC-l" and the curve marked "TREC-2 looking at top 200 only" shows signifi- cant performance improvement. Whereas this improvement could represent a difference in topics (the

TElEC-1 curve is for topics 51-100 and the TREC-2

curves are for topics 101-150), the TREC-2 topics are generally felt to be more difficult and therefore this improvement is likely to be an imderstatement of the

actual improvements.

Only two groups worked with less than the full document

collection. Figure 9 shows the results for the one group

with official TREC-2 category B results (the results from

UCLA were received after die deadline). This figure

shows the best results from New York University (see

Strzalkowski & Carballo paper), compared with a cate- gory B version of the Comell SMART results. The

"nyuirS" results reflect a very mtensive use of natural lan- guage processing (NLP) techniques, including a parse of

the documents to help locate syntactic phrases, contextsensitive expansion of the queries, and other NLP

unprovements on statistical techniques.

10
Best Automatic Adhoc

1.00

0.80 -

0.60

o

0.40

0.20 -

0.00

0.00 0.20 0.40 0.60

Recall

0.80

INQOOl ^dortQ2 _^ Brkly3

CLARTA^ cmlL2 ^

Isiasm

1.00

Best Manual Adboc

i.oo

0.80

§

1Ph

0.60

0.40 -

0.20 -

0.00

0.00 0.20

_^INQ002

_B- Vtcms2

0.40 0.60

Recall

siems2

CnQst2

0.80

CLARTM

TOPIC2

1.00

Figures. Best Automatic Adhoc Results. Figure 6. Best Manual Adhoc Results.

11
1.00

0.80

0.60

Performance Improvements in Adhoc

o9^ 0.40

0.20

0.00

0.0 0.2

TREC-1

TREC-2

0.4 0.6

Recall

0.8 1.0

TREC-2 looking at top 200 only

1.00

0.80

0.60

Adhoc Category B

o

0.40

0.20

0.00

0.00 0.20 0.40 0.60

Recall

cmlVB nyuirS

0.80 1.00

Figure 7. Typical Improvements in Adhoc Results. Figure 8. Category B Adhoc Results.

12
53 Routing Results

The routing evaluation used a subset of the training topics

(topics 51-100 were used) against the new disk of test documents (disk 3). There were 40 sets of results for routing evaluation, with 32 of them based on runs for the fuU data set. Of the 32 systems using the full data set, 23

used automatic construction of queries, and 9 used manual construction.

Figure 9 shows the recall/precision curves for the six TREC-2 groups with the highest non-interpolated average

precision using automatic construction of the routing

queries. Again three systems are based on the Comell

SMART system. The plot marked "cmlCl" is the actual SMART system, using the basic Rocchio relevance feed- back algorithms, and adding many terms (up to 500) from

the relevant training documents to the terms in the topic.

The "dortPl" results come from using a probabilisticallybased relevance feedback instead of the vector-space algorithm, and adding only 20 terms from the relevant documents to each query. These two systems have the best routing results. The "BrklyS" system uses logistic regres- sion on both the general frequency variables used in their adhoc approach and on the query-specific relevance data

available for training with the routing topics. The results marked "cityr2" are from City University, London (see

Robertson, Walker, Jones, Hancock-Beaulieu & Gafford

paper). This group automatically selected variable ntmi- bers of terms (10-25) from the training documents for each topic (the topics themselves were not used as term

sources), and then used traditional probabilistic reweighting to weight these terms. The "INQ003" results also use

probabilistic reweighting, but use the topic terms,

expanded by 30 new terms per topic from the training

documents. The results marked "lsir2" are more latent semantic indexing results from Bellcore. This run was made by creating a filter of the singular-value decomposition vector sum or centroid of all relevant documents for a

topic (and ignoring the topic itselO.

Figure 10 shows the recall^recision curves for the six TREC-2 groups with the highest non-interpolated average

precision using manual construction of the routing

queries. The results marked "INQ004" are from the INQRY system using an inferential combination of the "INQ003" queries and manually modified queries created from the topic. The "trw2" results represent an adaptation

of the TRW Fast Data Finder pattern matching system to allow use of term weighting (see Metder paper). The

queries were manually constructed and the term weighting was learned from the training data. The "gecrdl"

results from GE Research and Development Center (see

Jacobs paper) also come from manually constructed

queries, but using a general-purpose lexicon and the train- ing data to suggest input to the Boolean pattem matcher.

The results marked "CLARTM" are similar to the "CLARTM" adhoc results except that the training docu- ments were used as the source for thesaurus building, as opposed to using the top set of retrieved documents. The

"rutcombx" results from Rutgers University (see Belkin,

Kantor, Cool & Quatrain paper) come from combining 5

sets of manually generated Boolean queries to optimize

performance for each topic. The results marked

"T0PIC2" are from the TOPIC system and reflect die use

of an expert system working off specially-constructed

knowledge bases to improve performance.

As was the case with the adhoc topics, the automatic query construction methods continue to perform as well

as, or in this case, better than the manual construction

methods. A comparision of the two INQRY runs illus- trates this point and shows that all six results with manually generated queries perform worse than the six runs with automatically-generated queries. The availability of

the training data allows an automatic tuning of the queries

that would be difficult to duplicate manually without

extensive analysis.

Unlike the adhoc results, there are two runs ("cmlCl" and

"dortPl") that are clearly better than the others, with a sig- nificant difference between the "cmlCl" results and the "dortPl" results and also significant differences between

these results and the rest of the automatically-generated query results. In particular the Comell group's ability to effectively use many terms (up to 500) for query expansion was one of the most interesting findings in TREC-2

and represents a departure from past results (see Buckley,

Allan, & Salton paper for more on tiiis). As a final point, it should be noted that the routing results also represent significant improvements over die results from TREC-1. Figure 11 shows a comparison of results for a typical system in TREC-1 and TREC-2. Some of

this improvement is due to the improved evaluation tech- niques, but the difference between the curve marked

"TREC-1" and the curve marked "TREC-2 looking at top

200 only" shows significant performance improvement.

There is even more improvement for the routing results than for the adhoc results, due to better training data (mostiy non-existent for TREC-1) and to major efforts by

many groups in new routing algorithm experiments.

Only four groups worked with less than the full document

collection. Figure 12 shows the results for two of the groups in category B compared with a category B version

of the Cornell SMART results. These curves show the

results of runs from New York University (that were done

in a similar metiiod as that used for the adhoc results) and

results from DaUiousie University.

13
1.00

0.80

Best Automatic Routing

0.60

- o*—

4

COO<1> 0.40

0.20

0.00

0.00 0.20 0.40 0.60

Recall

0.80

1.00

0.80 -

0.60

cmlC1 dortP 1 ^

cityr2

INQ003 ^

Brkly5 lsir2

Best Manual Routing

o9> 0.40

0.20

0.00

0.00 0.20 0.40 0.60

Recall

0.80

_^ INQ004 ^trw2 _^ gecrd1 _^CLARTM^rutcombx ^TOPIC2

1.00

1.00

Figure 9. Best Automatic Routing Results. Figure 10. Best Manual Routing Results.

14
Performance Improvements In Routing

1.00 I

0.0 0.2 0.4 0.6 0.8 1.0 Recall

TREC-l TREC-2 looking at top 200 only

_^ TREC-2

Routing Category B

1.00 I

0.80 -

Figure 11. Typical Improvements in Routing Results. Figure 12. Category B Routing Results.

15
6. Some Preliminary Analysis

6.1 Introduction

The recall/precision ciirves shown in section 5 represent

the average performance of the various systems on the full sets of topics. It is important to look beyond these aver- ages in order to learn more about how a given system is performing and to discover some generalizable principles

of retrieval.

Individual systems are able to do this by performing fail- ure analysis (see Dumais paper in this proceedings for a good example) and by running specific experiments to test hypotheses on retrieval behavior within a given system.

However, additional information can be gained by doing some cross-system comparison: information about specific system behavior and information about generalized

information retrieval principles. One way to do this is to examine system behavior with respect to test collection

characteristics. A second method is to compare system

behavior on a topic by topic basis.

62 The Effects of Test Collection Characteristics

One particular test collection characteristic is the length of

documents, both the average length of documents m a collection, and the variation in document length across a collection. Document length has significant effect on system

performance. A term that appears 10 times in a "short" document is likely to be more unportant to that document

than if the same term appeared 10 times m a "long" document. Table 3 shows system performance across the dif- ferent document subcoUections for each of the adhoc topics, listing the total number of documents that were

retrieved by the system as well as the nxmiber of relevant documents that were retrieved. Two particular points can be seen from table 3. First, the

better systems retrieve about 50% relevant documents

from all the subcoUections except the Federal Register

(FR). For this subcollection the retrieval rates are in the 25% range because the varied length of these documents

makes retrieval difficult. The second point concerning table 3 is that the retrieval rate across the subcoUections is highly varied among the systems. For example the "Brkly3" results show that many fewer Federal Register documents and more AP

were retrieved than for the INQUERY system, whereas

the "CLARTA" results show more DOE abstracts and

fewer Wall Street Journal being retrieved. These "biases" towards particular subcoUections reflect the methods used

by systems such as the length normalization issues,

domain concentrations of terminology, and methods used

to "merge" results across subcoUections (often implicit

merges during mdexing).

A second test collection characteristic worth examining is the varied broadness and varied difficulty of Uie topics.

An analysis was done [Harman 1994] to find die topics for which the systems retrieved the lowest percentage of

the relevant documents on average. These topics are 61,

67, 76, 77, 81, 85, 90, 91, 93, and 98 for die routing topics

and 101, 114. 120, 121, 124. 131. 139, 140. 141, and 149

for the adhoc topics. Tables 4 and 5 show the top 8 sys- tem runs for the individual topics based in the average

precision (noninterpolated). These tables mix automatic,

manual, and feedback results for category A, and also cat- egory B results, so they should be interpreted carefully.

However they do demonstrate that no consistent patterns appear for the "hard" topics. The two best routing runs ("cmlCl" and "dortPl") only do weU on about half of

these topics, and the adhoc results are even more varied. Often systems that do not perform well on average are the top performing system for a given topic. This verifies

that, as usual, the variation across the topics is greater than the variation across systems.

6.3 Cross-System Analysis

Tables 4 and 5 not only show the wide variation in system

performance, but also raise several questions about system

performance in general.

1. Does better average performance for a system

result from better performance on most topics or from comparable performance on most topics and

significantly better performance on other topics?

2. If two systems perform simUarly on a given topic,

does that mean that they have retrieved a large

proportion of the same relevant documents?

3. Do systems that use "similar" approaches have a high overlap in the particular relevant documents

they rettieve?

4. And, if number 3 is not true, what are the issues

that affect high overlap of relevant documents?

Work is ongoing at NIST on tiiese questions and other

related issues.

16
Table 3. Number of Documents Retrieved/Relevant by Document Subcoilection

Run Tag AP DOE FR WSJ ZIFF

DiKiyj 07/79 y 1 ILL 1 847 /81

1

lOHZ/OJi 140/191 jty/ 1 z 1 citril 9 1 ^9 /Q7n 474/170 190/1/^ IZy/ lO 1loOJ/ 86^; /770 / /U 180/1 joU/i77/ / CIUIZ "^48/1 '^fi 9'\0/67 ZjU/O/ 1 814/7';6 10 It/ / JO joZ/ 189/170 1 /y

cityau 1 J / o/ /yr o'*/4^; 1 1^0/147 1601/661 lOUj/OOl 167/108 JO//1UO

ciiymi 1Q'?Q/199fi 94/91 ZH/Zi <i84/14/^ Jof/ 1 HO 9096/8 1

9

ZUZO/OiZ 497/171 tZ If I 1 D n APTA 40"? /1 70 419/0^ 17QS/S1Q 1'n6/1Q0 JjO/ lyu

9zu1 1I/XUO /I ns7/ -188/708 JOO/ZUO ii';/87 1760/890 1 /D7/OZU 107/991 J7 //ZZl

9979/091 9^4/1 ZiD^I 1 IZ19 1ji1 11//74/t 1761/680 400/1 81 9914/QXn 101/04 4S1/01 1787/718 1 / O // / JO '155/184 JJJ/ io*+ 1014/Q44 648/740 1 JJ/JJ 1 774/771 JU7/Z1 500/911

J

rm1V9 9 1 #^4/1 OS^ 687/716 70/79 / y/zz lUUU/OOZ 1 600/689 470/1 H l\JI I 04yr

9081 /I n'>'^ 30*^/160 471/78 *+/ J/ /o 1 8 1 8/8 1

S

lO iO/O IJ 191/166 JZJ/ lOO

357/171 186/44 1924/874 J'i^yJl 328/171 XIX prima 1 85/51 1364/110 2251/752 221/94

firitna9 1267/614 199/68 1210/125 2124/773 268/131 ^\fOf XUU

0'P/*rfl9 22S0/852 294/91 319/68 1952/743 185/77 X \J*Jf 1 1

XJJ. iV^nUi. 2140/1042 409/145 164/53 1875/839 412/181

HNCadS 2163/1286 306/159 171/67 1974/1005 386/237

9031/1071 206/1 07 297/1 1

5

2184/1023 282/151 Z<0^/ X ^ X 90R7/1 1 1

1

901 /1 90 976/1 1

1

Z / O/ 1 1

1

9141/1010 Zi*Ti/lUlU 005/177 Lyji L 1

1

Isial 9978/771 JO^87/11 ll^n94 194/0 iZt/U 1448/176 jOj/oi '561/61 isiasm 91/^8/1 zioo/iujz n^9 711/711 / 1 i/Zl 1 70/17 10U//07U 1 607 /600 /I /I /I /I o-j

'I'I'I/ lOJ nyuiri u/u 0/0 0/0 u/u jUUU/i ^000/1 160 jOU 0/0 U/U

nyuirz u/u 0/0 u/u 0/0 u/u '^000/1 '547 jUUU/iJt / 0/0 U/U

u/u 0/0 u/u 0/0 u/u '^OOO/I ^547 JUUU/l J't / 0/0 u/u

pircs3 9 1 no/1 09

1

1S8/1 '?9 JJOl ijZ 946/86 zto/oo 1 000/81*; lyyyiojj 988/1 ZOO/ 1 JJ' 10

9108/1014 "^49/1 48 2^4/8*5 9012/861 284/137 ^O'T/ Xj/

T\rCf*f\ 1 1000/1024 315/83 1XX/178/205 U/ 1377/980 x^ 1 1 1 y\j\j 1031/277 X V/»J X / ^ / / T^rpoi-* 1 1667/1024 695/83 381/205 1350/980 907/277

1 UlL^Uiiiu 1 1 090/368 181/79 iOl/ /z 112/18 1 1^/ xo 961/1 1

2

215/79

1 UlilllCC' 04*;/^00 ni /46 161/0 061/789 yuj/zoz

C/*ri on 1 90'?8/001 ZUJO/7V1 J574/180 jt/ioy 171/18 1 778/706 477/1 t / / / xoo

CI Amoi s>lcIIlSZ 999^/1 ZZZj/i it147/ 61 1 /7 1

8

uz/o 16^^^/770 lOJJ/ / /u 497/909

2238/1 173 654/208 53/7 1619/764 436/194

TMC8 2054/859 146/44 763/59 1472/526 565/183

TMC9 1923/802 77/29 975/63 1401/507 624/171

T0PIC2 2292/996 152/98 344/100 1762/889 384/229

UREKA2 385/215 0/0 4003/87 354/144 258/10

UREKA3 755/405 5/2 2654/67 1045/348 441/22

uicah 1612/628 234/104 797/137 1846/356 511/167

VTcms2 2110/1130 232/107 444/95 1859/894 355/169

totals 71354/4630 12073/669 21407/396 79396/3929 15504/1154

17
Table 4. System Rankings (using Average Precision) on Individual Topics

51 nyuir2 nyxiirl gecrdl T0PIC2 ADS2 cityr2 INQ004

52 INQ004 INQ003 Brkly4 pircs2 VTcms2 gecrdl pircsl trwl

53 gecrdl nyuir2 trw2 nyuirl CLARTM CLARTA dortPl INQ003

54 siemsl cmlRl schaul Brkly4 INQ003 cmlCl Isirl CLARTM

55 dortPl cmlRl cmlCl lsir2 dortVl CLARTM cityrl

/"TT A T^T* A CLARTA

56 trw2 dortPl dortVl INQ003 INQ004 HNCrtl cmlRl cmlCl

57 INQ003 lsir2 INQ004 trw2 cmlCl 1MC6 VTcms2 , 1T> 1

58 nyuir2 nyuirl rutcombx INQ003 lsir2 TVTf~\ f\f\ A INQ004 gecrdl TTJ 1 1 £ BrklyS

59 trw2 Brkly5 gecrdl Isirl ilNCrtl V Icmsz xUNLrtz isirz 60 dortPl dortVl rutcombx cmlRl LNQU04 cmlCl

61 T0PIC2 rutcombx Brkly4 idsra2 cityr2 Isul TNT^nri/i 1NQUU4 DrklyS

62 cmiRl cmlCl dortPl Isu^l CLARIA Brkly4 LLAKIM DrklyD

63 dortVl cmlCl pircs2 cmlRl pircsl siemsl HNCrtl J i Tl 1 dortPl

64 nyuir2 lsir2 INQ004 INQ003 Brkly5 cmlCl IT^ 1 cmlRl cityr2

65 cmlCl dortVl dortPl HNCrtl cmlRl trw2 HNCrt2 Isirl 66 pircs2 pircsl dortPl dortVl cmlRl cmlCl siemsl INQ004

67 cmlRl cmlCl INQ004 nyuir2 dortPl cityr2 lsir2 INQ003

68 Brkly5 cmlCl cityrl cityr2 trw2 INQ003 1

• lsir2

/~rT A T4T^ A CLARTA

69 erimrl Brkly5 dortVl cityr2 cityrl enmr2 1 " 1 lsu-1

T* 11 ^ Brkly4

70 TMC6 TMC7 rutcombx VTcms2 HNCrt2 T» 11 ^ Brkly5 INQ004 cityr2

71 cmlRl cmlCl HNCrt2 siemsl CLARTM HNCrtl A TiT* A CLARTA lsir2 72 IT* 1 cmlRl cmlCl dortPl siemsl INQ003 BrklyS cityrl

73 INQ003 , IT* 1 cmlRl cityr2 INQ004 cmlLl trw2 dortPl aortVl

74 cmlRl rutcombx cmlCl CLARTA BrklyS dortPl siemsl dortVl

75 cmlCl ADS2 r. ..—in 1 cmlRl 1 trwl lsu^2 dortPl cityr2 nyuir2

76 trw2 cityr2 T0PIC2 TMC6 TMC7 cmlCl ITl 1 cmlRl INQ003

77 cmlRl cmlCl INQ003 CLARTM dortVl dortPl

Tfc T /~\ f\f\ A INQ004 CLARTA

78 rutcombx T0PIC2 INQ004 CLARTM INQ003 dortVl pu:cs2 CLARTA

79 cityr2 cmlRl cmlCl INQ004 dortPl gecrdl

1

• lsir2 INQ003

80 trwl cmlCl cmlRl cityrl Brkly5 INQ003 INQ004 cityr2

81 gecrdl TMC7 TMC6 cityr2 trw2 VTcms2 HNCrt2 cityrl

82 CLARTM CLARTA trw2 Brkly5 pircsl pircs2 dortVl dortPl

83 T0PIC2 gecrdl trwl cmlCl HNCrtl cmlRl cityr2 cityrl

84 dortPl cmlCl lsir2 gecrdl cmlRl dortVl trwl VTcms2

85 cmlRl cmlCl dortPl Brkly5 trw2 nyuir2 dortVl siemsl

86 gecrdl VTcms2 lsir2 Isirl cityrl cmlRl cityr2 cmlCl

87 1

• lsu'2 gecrdl cityrl cityr2 HNCrtl BrklyS cmlCl HNCrt2

88 cmlCl cityr2 cmlRl T» 11 >1 Brkly4 dortPl lsir2 dortVl BrklyS

OA89 tnv2 nyuirl TOPIC2 TMC6 HNCrtl mcrl HNCrt2 gecrdl

90 gecrdl trwl _ ...-.1/^1 cmlLl cmlRl schaul VTcms2 BrklyS dortPl

91 trwl INQ004 schaul Ti 11 C Brkly5 trw2 T0PIC2 HNCrt2 HNCrtl

92 gecrdl cmlRl lsir2 cmlCl CLARTM CLARTA nyuirl INQ003

0"^ yj JTUILUIIIDa 1 iVlV^O trwl jDriciyj gecrui

94 lsir2 cmlCl cityr2 gecrdl INQ004 CLARTM trw2 cityrl

95 VTcms2 gecrdl cmlCl BrklyS cmlRl Brkly4 trwl siemsl

96 dortPl T0PIC2 cityrl dortVl cityr2 lsir2 cmlCl rutcombx

97 idsra2 HNCrtl nyuir2 dortPl HNCrt2 lsir2 cmlCl T0PIC2

98 HNCrtl HNCrt2 cmlCl trw2 DalTx2 INQ004 cmlRl dortPl

99 lsir2 cmlRl dortPl CLARTA cmlCl CLARTM dortVl cityr2

100 cmlCl cmlRl dortPl lsir2 dortVl CLARTA CLARTM Isirl

18
Table 5. System Rankings (using Average Precision) on Individual Topics

101 rutcombl VTcms2 cmlV2 INQ002 dortQ2 pircs3 Brkly3 CLARTM

102 cmlL2 cmlV2 VTcms2 siems3 dortL2 INQ002 siems2 CLARTM

103 siems3 siems2 schaul citril cmlV2 Isiasm HNCad2 HNCadl

104 dortQ2 CLARTM CLARTA pircs4 pircs3 dortL2 HNCad2 Isiasm

105 citri2 Isiasm citril siems2 siems3 cmIV2 schaul cniIL2

106 VTcms2 INQ002 INQOOl T0PIC2 pircs4 pircsB CLARTM dortL2

107 CnQstl CnQst2 rutcombl T0PIC2 VTcms2 INQ002 rutfmed CLARTM

108 citril dortQ2 siems3 VTcms2 siems2 HNCad2 schaul dortL2

109 dortL2 cmIL2 dortQ2 CLARTA CLARTM pircs3 cmlV2 pircs4

110 INQ002 INQOOl Brkly3 dortQ2 nyuir3 nyuir2 cityau siems2

111 CLARTA CLARTM INQOOl dortQ2 Brkly3 siems2 siems3 pircs4

112 INQ002 INQOOl VTcms2 nyuir2 nyuir3 HNCadl HNCad2 CnQst2

113 VTcms2 cnilL2 dortL2 cmlV2 nyuirl siems2 CLARTM INQ002

114 INQ002 cityau VTcms2 INQOOl siems3 siems2 Isial TOPia

115 nyuir2 nyuir3 nyuirl siems2 dortL2 cmlV2 siems3 cmlL2

116 VTcms2 CLARTA HNCad2 HNCadl siems3 siems2 CLARTM Brkly3

117 citri2 citril dortQ2 INQOOl TMC8 Isiasm gecrd2 schaul

118 nyuir2 nyuir3 nyuirl T0PIC2 citymf dortQ2 CLARTA INQOOl

119 nyuirl nyuir2 nyuir3 INQ002 INQOOl dortQ2 citymf VTcms2

120 citymf nyuii2 nyuir3 nyuirl CnQst2 CnQstl VTcms2 erima2

121 T0PIC2 CLARTM VTcms2 Brkly3 nyuirl prceol INQ002 rutfmed

122 siems2 siems3 INQ002 INQOOl dortQ2 Brkly3 CLARTM cnilV2

123 nyuirl nyuir2 nyuir3 CLARTA INQOOl INQ002 CLARTM pircs4

124 nyuir2 nyuir3 nyuirl dortL2 dortQ2 INQOOl Brkly3 TMC9

125 cmlV2 Brkly3 cmlL2 CLARTM siems3 CLARTA pircs4 pircs3

126 siems3 cmlL2 siems2 Brkly3 cmlV2 INQ002 CLARTM INQOOl

127 cityau Brkly3 CLARTA HNCad2 INQOOl INQ002 siems2 siems3

128 VTcms2 CLARTA siems3 siems2 CLARTM T0PIC2 citril Isiasm

129 INQOOl INQ002 cityau CLARTM siems2 Brkly3 cmIL2 CLARTA

130 INQ002 INQOOl dortQ2 cmlL2 pircs4 CLARTM dortL2 pircs3

131 T0PIC2 VTcms2 HNCadl HNCad2 siems3 Brkly3 siems2 INQ002

132 dortL2 INQOOl INQ002 citril citri2 dortQ2 HNCad2 cmlL2

133 CnQst2 CnQstl rutcombl pircs4 INQ002 pircs3 cityau INQOOl

134 cmlL2 dortL2 nyuirl nyuir2 nyuir3 INQ002 INQOOl dortQ2

135 nyuir2 nyuir3 nyuirl Brkly3 INQOOl INQ002 siems3 siems2

136 VTcms2 CnQstl CnQst2 CLARTM pircs4 CLARTA dortQ2 T0PIC2

137 CLARTA nyuir2 nyuir3 Brkly3 siems2 siems3 CLARTM nyuirl

138 nyuir2 nyuir3 rutCmed rutcombl nyuirl schaul gecrd2 citril 139 nyuir2 nyuir3 njojirl VTcms2 dortL2 HNCad2 dortQ2 HNCadl

140 nyuir2 nyuir3 nyuirl dortQ2 dortL2 INQ002 siems3 siems2

141 VTcms2 INQ002 CnQst2 INQOOl Brkly3 dortL2 dortQ2 CnQstl

142 dortQ2 siems2 cmIL2 VTcms2 siems3 CLARTM cmlV2 Brkly3

143 INQ002 INQOOl siems2 siems3 cmlL2 cmlV2 nyuir2 nyuir3

144 VTcms2 Brkly3 citymf cmlV2 siems3 Isiasm siems2 HNCad2

145 cmlL2 cmlV2 dortL2 CLARTM nyuirl siems3 siems2 dortQ2

146 Brkly3 siems3 siems2 Isiasm cmlV2 schaul CLARTM citril 147 HNCad2 HNCadl VTcms2 citril INQ002 INQOOl citymf CLARTA

148 Isiasm cmlL2 cmlV2 siems2 siems3 Brkly3 dortL2 dortQ2

149 nyuirl CnQst2 T0PIC2 CnQstl CLARTA rutfined Brkly3 rutcombl

150 cmlL2 dortQ2 CLARTM siems3 INQ002 INQOOl cmlV2 siems2

19
6.4 Summary

The TREC-2 conference demonstrated a wide range of

different approaches to the retrieval of text from large

document collections. There was significant improvement

in retrieval performance over that seen in TREC-1. especially in the routing task. The availability of large

amounts of training data for routing allowed extensive

experimentation in the best use of that data, and many dif- ferent approaches were tried in TElEC-2. The automatic

construction of queries from the topics continued to do as well as, or better than, manual construction of queries,

and this is encouraging for groups supportiag the use of

simple natural language mterfaces for retrieval systems.

How well is the TREC initiative meeting its goals? There

is certainly mcreased research usiog a much larger collection than had previously been tested. This leads not only

to discovering interesting research problems, but also to developmg algorithms that are ripe for transfer into commercial systems. The conference itself provided the opportunity for this; there was open exchange between

the research groups in universities and the research groups

in commercial organizations and this is a very critical part

of technology transfer.

There will be a third TREC conference in 1994, and all the systems that participated in TREC-2 will be back,

along with additional groups.

7. References

Hannan, D. (1993) (Ed.).The First Text REtrieval Confer- ence (TREC-1). National Institute of Standards and Technology Special Publication 500-207,

Harman, D. (1994). Data Preparation. In: Merchant R. (EdXThe Proceedings of the TIPSTER Text Program - Phase I. San Mateo, California: Morgan Kaufrnann Publishing Co., 1994.

Katzer. J., McGill, M.J., Tessier, J.A., Frakes, W.. and

DasGupta, R (1982). A Study of the Overiap among Document Representations. Information Technology:

Research and Development, 1(2), 261-274.

Salton, G. and McGill, M. (1983). Introduction to Modern

Information Retrieval. New York, NY.: McCiraw-Hill.

Sparck Jones, K. and Van Rijsbergen, C. (1975). Report on the Need for and Provision of an "Ideal" Information

Retrieval Test Collection, British Library Research and

Development Report 5266, Computer Laboratory, University of Cambridge. 1993.

20
Okapi at TREC-2

S E Robertson* S Walker* S Jones* M M Hancock-Beaulieu* M Gatford*

Advisers: E Michael Keen (University of Wales, Aberystwyth), Karen Sparck Jones (Cambridge University), Peter

Willett (University of Sheffield)

1 Introduction

This paper reports on City University's work on the

TREC-2 project from its commencement up to November 1993. It includes many results which were obtained

after the August 1993 deadline for submission of official results. For TREC-2, as for TREC-1, City University used

versions of the Okapi text retrieval system much as de- scribed in [2] (see also [3, 4]). Okapi is a simple and

robust set-oriented system based on a generalised probabilistic model with facilities for relevance feedback, but

also supporting a full range of deterministic Boolean and

quasi-Boolean operations. For TREC-1 [1] the "standard" Robertson-Sparck

Jones weighting function was used for all runs (equation 1, see also [5]). City's performance was not out- standingly good among comparable systems, and the

intention for TREC-2 was to develop and investigate a number of alternative probabilistic term-weighting functions. Other possibilities included varieties of query ex- pansion, database models enabling paragraph retrieval and the use of phrases obtained by query parsing.

Unfortunately, a prolonged disk failure prevented re- alistic test runs until almost the deadline for submission

of results. A full inversion of the disks 1 and 2 database was only achieved a few hours before the final auto- matic runs. None of the new weighting functions (Section 1.1) was properly evaluated until after the results had been submitted to NIST; we have since discovered

that several of these models perform much better than

the weighting functions used for the official runs, and

most of the results reported herein are from these later runs.

1.1 The system

The Okapi system comprises a search engine or basic

search system (BSS), a low level interface used mainly

for batch runs and a user interface for the manual search

'Centre for Interactive Systems Research, Department of In- formation Science, City University, Northampton Square, London

EClV OHB, UK

experiments (Section 5), together with data conversion and inversion utilities. The hardware consisted of

Sun SPARC machines with up to 40 MB of memory,

and, occasionally, about 8 GB of disk storage. Several

databases were used from time to time: full disks 1 and

2, AP (disk 1) and WSJ (disk 1), full disk 3. All in- verted indexes included complete within-document positional information, enabling term frequency and term

proximity to be used. Typical index size overhead was

around 80% of the textfile size. Elapsed time for in- version of disks 1 and 2 Wcis about two days. Running

a single topic with evaluation averaged from about one minute to ten minutes, depending strongly on the number of query terms. All preliminary evaluation used the

"old" SMART evaluation program. Runs tabulated in

this paper used an early version of the new evaluation

program, for which we are grateful to Chris Buckley of

Cornell University.

2 Some new probabilistic

models

Statistical approaches to information retrieval have tra- ditionally (to over-simplify grossly) taken two forms:

(a) approaches based on formal models, where the

model specifies an exact formula;

(b) ad-hoc approaches, where formulae are tried be- cause they seem to be plausible.

Both categories have had some notable successes. A

more recent variant is the regression approach of Fuhr

and Cooper (see, for example, [6]), which incorporates

ad-hoc choice of independent variables and functions

of them with a formal model for assessing their value

in retrieval, selecting from among them and assigning

weights to them.

One problem with the formal model approach is that

it is often very difficult to take into account the wide

variety of variables that are thought or known to influ- ence retrieval. The difficulty arises either because there

is no known basis for a model containing such variables, or because any such model may simply be too complex

to give a usable exact formula.

One problem with the ad-hoc approach is that there is little guidance as to how to deal with specific variables

one has to guess at a formula and try it out. This

21
problem is also apparent in the regression approach

although "trying it out" has a somewhat different sense here (the formula is tried in a regression model, rather

than in a retrieval test). The discussions of Sections 2.1 and 2.3 exemplify an

approach which may offer some reconciliation of these

ideas. Essentially it is to take a formal model which

provides an exact but intractable formula, and use it to suggest a much simpler formula. The simpler formula

can then be tried in an ad-hoc fashion, or used in turn in a regression approach. Although we have not yet taken

this latter step of using regression, we believe that the

present suggestion lends itself to such methods.

2.1 The basic model

The basic probabilistic model is the traditional rele- vance weight model [5] , under which each term is given a weight as defined below, and the score (matching value)

for each document is the sum of the weights of the

matching terms:

w = log

{r + 0.b)/{R-r + 0.5)

{n-r + 0.b)/{N -n-R+r + 0.5)

(1)

where

N is the number of indexed documents; n the number of documents containing the term; R the number of known relevant documents;

r the number of relevant documents containing

the term.

This approximates to inverse collection frequency

(ICF) when there is no relevance information. It will be referred to below (with or without relevance infor- mation) as w^^\

2.2 The 2-Poisson model and term

frequency

One example of these problems concerns withindocument term frequency (tf). This variable figures in a number of ad-hoc formulae, and it seems clear that

it can contribute to better retrieval performance. However, there is no obvious reason why any particular function of if should be used in retrieval. There is not much

in the way of formal models which include a tf component; one which does is the 2-Poisson model [7, 8]. The 2-Poisson model postulates that the distribution

of within-document frequencies of a content-bearing

term is a mixture of two Poisson distributions: one set of documents (the "elite" set for the particular term,

which may be interpreted to mean those documents

which can be said to be "about" the concept represented

by the term) will exhibit a Poisson distribution of a cer- tain mean, while the remainder may also contain the

term but much less frequently (a smaller Poisson mean).

Some earlier work in this area [8] attempted to use an

exact formula derived from the model, but had limited

success, probably partly because of the problem of esti- mating the required quantities. The approach here is to use the behaviour of the exact formula to suggest a very much simpler function of if which behaves in a similar way.The exact formula, for an additive weight in the style

of w^^\ of a term t which occurs if times, is

, (p'A*^e-- + {1- p')ii'^ e-^){q'e-^ + {I - gQe"^) W = log -^^ ; -, — ^ {q'X^fe-^ + (1- q')n^fe-''){p'e->^ + (1 - P'^'")

(2) where

A is the Poisson mean for tf in the elite set for

t;

_

fj, is the Poisson mean for tf in the non-elite

set; p' is the probability of a document being elite for t given that it is relevant;

q' is the probability of a document being elite given that it is non-relevant. As a function of tf, this can be shown to behave as

follows: it is zero for if = 0; it increases monotonically

with tf, but at an ever-decreasing rate; it approaches an

asymptotic maximum as if gets large. The maximum

is approximately the binary independence weight that

would be assigned to an infallible indicator of eliteness. A very simple formula which exhibits similar behaviour is tf/{if+ constant). This has an asymptotic

limit of unity, so must be multiplied by an appropriate

binary independence weight. The regular binary independence weight for the presence/absence of the term

may be used for this purpose. Thus the weight becomes

w =

if -w(1) (3)

{ki + if)

where ki is an unknown constant. Several points may be made concerning this argu- ment. It is not by any stretch of the imagination

a strong quantitative argument; one may have many

reservations about the 2-Poisson model itself, and the

transformations sketched above are hardly justifiable in any formal way. However, it results in a modification of

the binary independence weight which is at least plausible, and has just slightly more justification than plausibility alone. The constant in the formula is not in any way

determined by the argument. The effect of choice of constant is to determine the strength of the relationship

between weight and tf : a large constant will make for a

relation close to proportionality (where tf is relatively

22
small); a small will mean that if has relatively little effect on the weight (at least when if > 0, i.e. when the

term is present).

Our approach has been to try out various values of

ki (around 1 may be about right for the full disks 1 and

2 database). However, in the longer term we hope to use regression methods to determine the constant. It

is not, unfortunately, in a form directly susceptible to the methods of Fuhr or Cooper, but we hope to develop

suitable methods.

2.3 Document length

The 2-Poisson model in effect assumes that documents

(i.e. records) are all of equal length. Document length

is a variable which figures in a number of weighting for- mulae.

We may postulate at least two reasons why documents might vary in length. Some documents may simply cover more material than others; an extreme version

of this hypothesis would have a long document consist- ing of a number of unrelated short documents concate- nated together (the "scope hypothesis"). An opposite

view would have long documents like short documents,

but longer: in other words, a long document covers a

similar scope to a short document, but simply uses more

words (the "verbosity hypothesis").

It seems likely that real document collections contain a mixture of these effects; individual long documents

may be at either extreme or of some hybrid type. All the discussion below assumes the verbosity hypothesis; no progress has yet been made with models based on

the scope hypothesis.

The simplest way to deal with this model is to take

the formula above, but normalise if for document length

(dl). If we assume that the value of ki is appropriate

to documents of average length (avdl), then this model

can be expressed as

w

tf

fci X dl avdl + if)-w(1) (4)

A more detailed analysis of the effect on the Poisson

model of the verbosity hypothesis is given in Appendix

7.4. This shows that the appropriate matching value for a document contains two components. The first component is a conventional sum of term weights, each term

weight dependent on both if and dl; the second is a cor- rection factor dependent on the document length and

the number of terms in the query (rag), though noi on

which terms match. A similar argument to the above

for if suggests the following simple formulation:

(avdl — dl) correction factor = x nq- —- (5)

(avdl + dl) ^

^

where k2 is another unknown constant.

Again, k2 is not specified by the model, and must

(at present, at least) be discovered by trial and error. Values in the range 0.0-0.3 appear about right for the

TREC databases (if natural logarithms are used in the term-weighting functions^), with the lower values being

better for equation 4 termweights and the higher values

for equation 3.

2.4 Query term frequency and query

length

A similar approach may be taken to within-query term

frequency. In this case we postulate an "elite" set of

queries for a given term: the occurrence of a term in the

query is taken as evidence for the eliteness of the query

for that term. This would suggest a similar multiplier

for the weight:

w =

qtf

(^3 + qtf)

w(1) (6)

In this case, experiments suggest a large value of ^3

to be effective—indeed the limiting case, which is equivalent to w = qtf X w^^^ (7)

appears to be the most effective. We may combine a formula such as 6 or 7 with a document term frequency formula such as 3. In practice

this seems to be a useful device, although the theory

requires more work to validate it.

2.5 Adjacency

The recent success of weighting schemes involving a term-proximity component [9] has prompted consideration of including some such component in the Okapi

weighting. Although this does not yet extend to a full Keen-type weighting, a method allowing for adjacency

of some terms has been developed.

Weighting formulae such as w^^^ can in principle be

applied to any identifiable and searchable entity (such

as, for example, a Boolean search expression). An obvious candidate for such a weight is any identifiable phrase. However, the problem lies in identifying suit- able phrases. Generally such schemes have been applied

only to predetermined phrases (e.g. those given in a dic- tionary and identified in the documents in the course of

indexing). Keen's methods would suggest constructing

phrases from all possible pairs (or perhaps larger sets)

of query terms at search time; however, for queries of

the sort of size found in TREC, that would probably

generate far too many phrases.

The approach here has been to take pairs of terms

which are adjacent in the query as candidate phrases.

^ To obtain weights within a remge suitable for storage as 16-bit

integers, the Okapi system uses logarithms to base 2°'^

23
The present Okapi allows adjacency searches, so a phrase that is not specifically indexed can be searched,

and assigned a weight in the usual Okapi fashion as if

it had been indexed.

One problem with that approach is that the single

words that make up the phrase will probably also be

included in the query, and that suggests that a document which contains the phrase will be overweighted,

as it will be given the weight assigned to the phrase

in addition to the individual term weights. So in the

present experiments the weight assigned to the phrase

has been adjusted downwards, by deducting the weights

of the constituent terms, to allow for the fact that the

individual term weights have necessarily been added.

Where this correction would give a negative weight to the phrase, it has been adjusted again to an arbitrary

small positive number.

2.6 Weighting functions used

More than 20 combinations of the weighting functions

discussed above were implemented at one time or another. Those mentioned in this paper are listed here. For brevity, most of the functions are referred to as BMnn (Best Match).

BMO: Flat, or quorum, weighting. Each term is given

the same weight.

BMl: w^^^ termweights.

BM15: 2-Poisson termweights as equation 3 with document length correction as equation 5. BMll: 2-Poisson termweights with document length

normalisation as equation 4^

.

3 Document processing

For TREC-1 City used an elaborate 25-field structure which was intended to make all the disparate datasets on the CDs fit a unified model. It would, for example, have been possible to restrict searches to "title",

"headline" etc. In the event only the TEXT was used. For TREC-2, fields which looked useful for searching were simply concatenated into one long field. For most

datasets fields other than DOCNO and TEXT were

ignored, but the SJM LEAD PARAGRAPH, the Ziff SUMMARY and a few additional fields from the Patents

records were included. This was done using a simple perl

script (in contrast to the TREC-1 conversion program

which used lex, yacc and C). Most of the known data er- rors were handled satisfactorily, although for some rea- son there still remained a few duplicate DOCNOs from

disk 1 and/or 2.

^In theory there was also an equation 5 document length cor- rection, but the best value of k2 was found to be zero.

4 Automatic query processing

4.1 Ad-hoc

A large number of evaluation runs have been done to investigate

• the effect of query term source

• the use of a query term frequency (qtf) component

in term weighting, and

• the use of algorithmically derived term pairs.

4.1.1 Derivation of queries from the topics

Topic processing was very simple. An program (writ- ten in awk) was used to isolate the required topic

fields, which were then parsed and the resulting terms

stemmed in accordance with the indexing procedures of

the database to be searched. A small additional stop list was applied to the NARRATIVE and DESCRIPTION

fields only. If required, the procedure also output pairs

of adjacent terms which occur in the same subfield of

the topic and with no intervening punctuation. For ex- ample the command

get_qterms 70 trecl2_93 ted pairs=l

applied to

<title> Topic: Surrogate Motherhood

<desc> Description:

Document will report judicial proceedings and

opinions on contracts for surrogate motherhood.

<con> Concept(s):

1. surrogate, mothers, motherhood

2. judge, lawyer, court, lawsuit, custody, hearing, opinion, finding

(topic 70)

gave

70 19 desc:l:contract:l 70 19 con:l:court:l

70 19 con:l:custodi:l

70 19 con:l:find:l

70 19 con:l:hear:l

70 19 con:l:judg:l

70 19 desc:l:judici:l

70 19 con:l:lawsuit:l

70 19 con:l:lawyer:l

70 19 con:l:mother:l

70 19 tit:l:motherhood:3

70 19 con:l:opinion:2

70 19 desc:l:proceed:l

70 19 tit:l:surrog:3

70 19 desc : 2 :contract :surrog: 1

24
70:19:desc:2:ju<lici:proceed:l

70:19:desc:2:opinion:contract:l

70 : 1 9 :desc: 2 :proceed :opinion : 1 70 : 1 9 :tit :2 :surrog:motherhood :2

where the fields are topic number^ topic length (number

of terms counting repeats but not pairs), source field (in precedence order TITLE > CONCEPTS > NARRATIVE > DESCRIPTION > DEFINITIONS), number of terms, term . .

. , frequency of this term or pair in the topic.

4.1.2 Document £ind query term weighting

Table 1 shows the effect of varying query term source

fields when no account is taken of within-query term

frequency.

Some tentative conclusions can be drawn: adding TI- TLE to CONCEPTS improves most measures slightly;

TITLE alone works well in a surprising proportion of

the topics; the DESCRIPTION field is fairly harmless

used in conjunction with CONCEPTS, but NARRATIVE and DEFINITIONS are detrimental. (TIME and

NATIONALITY fields, which are occasionally present, were never used.) This really only confirms what may

be evident to a human searcher: that CONCEPTS consists of search terms, but most of the other fields apart

from TITLE are instructions and guidance to relevance

assessors. A sentence such as "To be relevant, a document must identify the case, state the issues which are or were being decided and report at least'one ethical or legal question which arises from the case." (from

the NARRATIVE field of topic 70) can only contribute

noise. However, when a within-query term frequency {qtf)

component is used in the term weighting, the infor- mation about the relative importance of terms gained

from the use of all or most of the topic fields seems to outweigh the detrimental effect of noisy terms such as

"identify", "state", "issues", "question". Some results are summarised in Table 2. A number of values of were tried in equation 6, and a large value proved best

overall, giving the limiting case (equation 7), in which

the term weight is simply multiplied by qtf.

Many combinations of the weighting functions dis- cussed in Section 1.1, as well as others not described

here, were first tested on the AP and/or WSJ databases.

Some of them were eliminated immediately. The function defined as BM15 gave almost uniformly better re- sults than w'^^\ after suitable values for the constants had been found. BMll appeared slightly less good than

BM15 on the small databases, but later runs on the

large databases showed that, with suitable choice of

constants, it was substantially, though not uniformly,

better. This may be a consequence of the greater variation in document lengths found in the large databases.

Table 3 compares the more elaborate term weighting

functions with the standard w^^^ weighting and with a

baseline coordination level run. Some work was done on the addition of adjacent pairs

of topic terms to the queries (see Section 2.5). A number of runs were done, using several different ways of adjusting the "natural" weights of adjacent pairs. There

was little difference between them, and the results are

at best only slightly better than those from single terms

alone (Table 3). There was also little difference between

using all adjacent pairs and using only those pairs which

derive from the same sentence of the topic, with no in- tervening punctuation.

4.2 Routing

Potential query terms were obtained by "indexing" all the known relevant documents from disks 1 and 2; the

topics themselves were not used (nor were known nonrelevant documents). These terms were then given w^^^ weights and selection values [11] given by x w^^^ where

r and R are as in equation 1. A large number of retrospective test runs were per- formed on the complete disks 1 and 2 database, in which

the number of terms selected and the weighting function were the independent variables. Overall, there was little difference in the average precision over the range 10-25

terms. This is consistent with the results reported by

Harman in [10]. With regard to weighting functions,

BMl was slightly better than BM15. However, looking at individual queries, the optimal number of terms

varied between three (several topics) and 31 (topic 89)

with a median of 11; and BM15 was better than BMl

for 27 of the topics.

Two sets of official queries and results were produced.

For the cityrl run, the top 20 terms were selected for each topic and the weighting function was BMl. For

cityr2 the test runs were sorted for each topic by precision at 30 documents within recall within average precision, and the "best" combination of number of terms

and weighting function was chosen. When evaluated

retrospectively against the full disks 1 and 2 database

the cityr2 queries were about 17% better on average

precision and 10% better on recall than the cityrl. The

official results (first and second rows of Table 4) show

a similar difference. Later, both sets of queries were

repeated using BMll instead of the previous weighting

functions (third and fourth rows of the table). These

final runs both show substantially better results than

either of the official runs.

25
Table 1: Effect of varying query term sources (no query term frequency component)

Query % of tops where

source ave Igth Ave Prec Prec at 5 Prec at 30 Prec at 100 R-Prec Recall AveP > median

TC 30.3 0.300 0.624 0.536 0.440 0.349 0.683 66

C 26.7 0.296 0.636 0.524 0.436 0.346 0.686 58

TCD 39.7 0.297 0.592 0.519 0.429 0.340 0.667 62

TCND 81.0 0.263 0.612 0.485 0.394 0.306 0.605 48

TCN 71.6 0.262 0.624 0.481 0.397 0.309 0.604 50

TCNDDef 86.3 0.257 0.580 0.468 0.387 0.303 0.604 46

TN 44.9 0.181 0.500 0.418 0.320 0.245 0.491 26

TND 54.4 0.179 0.492 0.403 0.317 0.243 0.491 24

TD 13.1 0.170 0.428 0.381 0.297 0.244 0.492 28

T 3.6 0.165 0.380 0.343 0.271 0.233 0.471 32

Terms: single. Document termweights: BMll. Database: disks 1 and 2. Topics 101 -150

Query average length is the average number of terms taking account of repeats

Table 2: Effect of varying query term sources (with query term frequency component)

Query Weight % of tops where

source function AveP P5 P30 PlOO RP Rcl AveP > median

TCND BMll 0.360 0.652 0.569 0.4T9 0.401 0.754 92

TCN BMll 0.356 0.644 0.565 0.482 0.399 0.749 92

TCNDDef BMll 0.354 0.648 0.559 0.474 0.395 0.751 92

TCD BMll 0.353 0.644 0.565 0.481 0.394 0.750 90

TC BMll 0.335 0.636 0.560 0.468 0.375 0.723 86

TC BM15 0.284 0.560 0.485 0.416 0.336 0.685 56

TND BMll 0.283 0.556 0.503 0.414 0.338 0.652 60

TN BMll 0.274 0.556 0.497 0.399 0.331 0.643 56

TC BMl 0.232 0.504 0.435 0.361 0.289 0.601 28

Document term weights were multiplied hy qif, equivalent to large in eqn 6 Terms: single. Database: disks 1 and 2. Topics 101-150

Table 3: Effect of different document term weighting functions: single terms and adjacent pairs

Weight % of tops where

function Terms AveP P5 P30 PlOO RP Rcl AveP > median

BMll singles

+ "natural" pairs 0.307 0.628 0.541 0.448 0.358 0.696 62

BMll singles

+ all adj pairs 0.304 0.612 0.544 0.447 0.357 0.694 62

BMll singles 0.300 0.624 0.536 0.440 0.349 0.683 66

BM15 singles 0.227 0.500 0.434 0.351 0.285 0.595 38

BMl singles 0.199 0.468 0.416 0.326 0.261 0.542 22

BMO singles 0.142 0.412 0.336 0.270 0.209 0.411 12 "Natural" means adjacent in the same sentence of the topic with no intervening punctuation

Query term source: TC. ^f/ component : none. Database: disks 1 and 2. Topics: 101-150

26
Table 4: Some routing results

Weight Number % of tops where

function of terms AveP P5 P30 PlOO RP Rcl AveP > median

BM1/BM15 variable 0.356 0.692 0.561 0.449 0.388 0.680 78

BMl top 20 0.315 0.628 0.533 0.432 0.361 0.648 70

BMll variable 0.394 0.700 0.599 0.481 0.429 0.713 92

BMll top 20 0.362 0.684 0.605 0.459 0.397 0.707 80

Best predictive run for comparison (BMll, qif wi th large A:3, source TCD)

0.300 0.612 0.524 0.394 0.345 0.632 68

Database: disk 3. Topics: 51-100

5 Manual queries with feedback

5.1 The user interface

The interface allowed the entry of any number of

find commands operating on "natural language" search terms. By default, the system would combine the result- ing sets using the BM15 function described in Section

2.6, but any operation specified by the searcher would

override this. All user-entered terms were added to a pool of terms for potential use in query expansion. Every set produced had any documents previously seen by

the user removed from it. The show (document display) command displayed

the full text of a single document (or as much as the

user wished to see) with the retrieval terms highlighted

(sometimes inaccurately). Unless specified by the user

this would be the highest-weighted remaining document

from the most recent set. At the end of a document dis- play the relevance question

"Is this relevant (y/n/?)"

appeared; the system counted documents eliciting the

"?" response as relevant^. The DOCNO was then out- put to a results file, together with the iteration number.

Once some documents had been judged relevant the

extract command would produce a list of terms drawn

from the pool consisting of user-entered terms and terms

extracted from all relevant documents. Terms in the

pool were given w^^> weights. User-entered terms were

weighted as if they had occurred in four out of five fic- titious relevant documents (in addition to any real rele- vant documents they might have been present in). Thus

for user-entered terms the numerator in equation 1 be- comes (r-|-4-|-0.5)/(i2+5 - r-4-1-0.5) [2]. Query expansion terms were selected from the term

pool in descending order of the selection value [11] termweight x {r + 4)/{R + 5) for user-entered terms,

''It was possible for seaxchers to change their minds about the

relevajice of a docimient. Subsequent feedback iterations handled

this correctly, but the DOCNO would be duplicated in the search output. This appears to have led to some minor errors in the frozen ranks evaluation in a few topics.

otherwise termweight x r/R, subject to not all documents containing the term having been displayed, and

the term not being a semi-stopword^ (unless it was en- tered by the user). A maximum of 20 terms was used. These selected terms were then used automatically in an expansion search, again with the BM15 weighting

function.

Each invocation of extract used all the available rele- vance information, and there was no "new search" com- mand. This was intended to encourage compliance with

the TREC guidelines; it was not possible for a dissatisfied user to restart a search. When the searcher de- cided to finish, after some sequence of find, show and

extract commands, the results command invoked a final iteration of extract (provided there had been at least three positive relevance judgments). Finally, the top

1000 DOCNOs from the current set were output to the

results file. Apart from the aforementioned commands,

users could do info sets and history.

5.2 Searchers and search procedure

The searches were done by a panel of five staff and re- search students from City University's Department of

Information Science. Search procedure was not rigidly

prescribed, although some guidelines were given. There was a short briefing session and searchers were encour- aged to experiment with the system before starting.

Procedures seemed to be considerably influenced by in- dividual preferences and styles. Some searches were done collaboratively.

Searchers tried to find relevant documents by any means they liked within a single session. The number of

iterations of query expansion varied between zero and

four, with a mean of two. The IDs of all documents

looked at were output to the results file, together with

the iteration number. At the end of the session, if at

least three relevant documents had been found the sys- tem did a final iteration of query expansion and output

*Semi-stopwords are words which, while they may be useful

secirch terms if entered by a user, are likely to be detrimental if used in query expansion: nimierals, month-names, common adverbs etc.

27
the top 1000 IDs; if less than three the top 1000 from

the set which was finally "current" were output.

There seemed to be an impression that the new topics (topicsS) are more difficult than the old. Results

may also have been affected by the huge stoplist which was being used at that time because of a breakdown

of the only disk large enough to hold the very large

scratch files generated during inversion. Lack of the

number "6" affected one topic, days of the week an- other ("Black Monday"). The searcher was urged to leave "Black Monday" to the end in case we were able

to reindex before the deadline, but she decided to try it and thought it worked quite well. An edited transcript of one searcher's notes is given

below as Appendix B.

5.3 Results

The official results of the manual run (Table 5) are dis- appointing, with average precision 0.232 (60% of topics

below median), precision at 100 docs 0.4 and recall 0.59. The final iteration was later re-run with BMll instead

of BM15, and the results combined with the feedback

documents from the original searches for a frozen ranks

evaluation^. This did somewhat better on a majority

of the topics, but overall the manual results were very poor compared to some of the automatic runs.

6 Other experiments

6.1 Query modification without

relevance information

Some iterative automatic ad hoc runs were done in which the top 10-50 documents obtained by the best

existing method were used (a) as a source of additional

terms and (b) as a source of "relevance" information for the w^^^ weight calculation.

Expansion terms were selected as described in Section

4.2, in descending order of x w^^\ The maximum

number of additional terms was set at half the number

of query terms. For many of the topics most of the top

terms extracted from the feedback documents were in any case topic terms, so the number of additional terms was small.

Example (topic 112)

Topic 112: Funding biotechnology

30 feedback documents used

In the table which foUows, term sources are given either as

doc, in the case of expansion terms, or as a topic field, where

tit > con > nar > desc. In this example, final weights involve a gt/ component, and were obtained using equation 6 with

^ There were two topics where the searcher found no relevant documents, so for these topics the original results were inserted.

A:3 = 8 (the resulting weight was multipUed by ks to obtain

adequate granularity in an integer representation). For ex- pansion terms, qtf was taken as 1 and the same correction apphed.

Weights

Term Src qtf # docs Orig Final

biotechnologi tit 9 30 765 145 614

invest con 4 29 148 80 213 fund tit 2 23 78 - 55 88

capit nar 2 21 78 51 81 pharmaceut doc (0) 15 73 64

ventur nar 1 21 55 67 59

financi. .

.

nar 2 17 64 36 57

startup. .

.

nar 1 11 70 62 55

research nar 1 26 35 61 54

financ doc (0) 15 _ 54 48

partner doc (0) 17 - 55 48 drug doc (0) 18 - 53 47

investor doc (0) 19 _ 52 46

provid nar 3 14 66 21 45

firm nar 1 22 36 50 44

technologi doc (0) 23 - 50 44 company. .

.

doc (0) 28 - 48 42 academ nar 1 4 73 48 42 corpor nar 2 9 76 26 41 monei desc 1 18 37 43 38

stock nar 1 20 33 43 38

industri. .

.

doc (0) 23 42 37

develop doc (0) 25 42 37

laboratori nar 1 9 51 39 34

quantifi nar 1 1 82 39 34

profit nar 1 14 40 38 33

enterpr nar 1 4 59 33 29 estabUsh nar 1 10 38 29 25 arena* nar 2 0 148 15 24 data nar 4 6 108 8 21

sale nar 1 12 30 24 21

loss nar 1 7 39 22 19 government. .

.

nar 1 13 24 20 17

assist nar 1 6 39 20 17 much desc 1 11 28 20 17 answer desc 1 2 52 16 14

follow nar 1 7 26 9 8

rel* desc 1 1 52 9 8 eg* nar 1 0 67 8 7 question desc 1 3 37 8 7 worldwid* nar 2 0 126 4 6 division* nar 1 2 41 6 5

figur* nar 1 2 41 5 4

Here, nine of the 43 terms^ are not from the topic. The

starred terms were not used in the final search because

their selection value w^^^ x is zero (to the nearest

integer). For this topic, the additional terms were

beneficial and reweighting alone rather neutral.

The terms followed by ellipses represent synonym classes

28
Terms Wts I AveP P5 P30 PlOO RP Rcl

All Final 0.407 1.000 0.867 0.640 0.457 0.739 Topic Final 0.362 1.000 0.733 0.620 0.440 0.698 Topic Prig | 0.373 0.600 0.800 0.700 0.433 0.680

6.2 Stemming

A comparison was made on the AP database between

the normal Okapi stemming which removes many suffixes and a "weak" stemming procedure which only conflates singular and plural forms and removes "ing" endings. For some weighting functions weak stemming in- creased precision by about 2% and decreased recall by

about 1%, but the observed diff'erence is unlikely to be

significant.

6.3 Stoplists

Some runs were done on the AP databeise to investigate

the effect of stoplist size. A small stoplist consisted of

the 17 words

a, the, an, at, by, into, on, for, from, to,

with, of, and, or, in, not, et

and a large one contained 209 articles, conjunctions,

prepositions, pronouns and verbs. There was no significant difference in the results of the

runs, but the index size was about 25% greater with the

small stoplist.

7 Conclusions and prospects

7.1 The new probabilistic models

The most significant result is perhaps the great improvement in the automatic results brought about by the new

term weighting models. In the ad-hoc runs, with no qtf

component, BM15 is 14% better than BMl on average

precision and about 9% better on high precision and recall. The corresponding figures for BMll are 51% and

34% (Table 3). For the routing runs, where a considerable amount of relevance information had contributed to the term weights, the improvement is less, but still very

significant (Table 4). For the manual feedback searches

(Table 5) there was a small improvement when they were re-run with BMll replacing BM15 in the final it- eration. The drawback of these two models is that the theory

says nothing about the estimation of the constants, or rather parameters, and k2. It may be assumed that

these depend on the database, and probably also on the nature of the queries and on the amount of relevance

information available. We do not know how sensitive

they are to any of these factors. Estimation cannot be

done without sets of queries and relevance judgments,

and even then, since the models are not linear, they do

not lend themselves to estimation by logistic regression.

The values we used were arrived at by long sequences

of trials mainly using topics 51-100 on the disks 1 and

2 database, with the TREC-1 relevance sets.

Discussion

The main motive for experimenting with this type of

query expansion is that it is one way of finding terms which are in some sense closely associated with the

query as a whole. It does not fit particularly well with

the Robertson/Sparck Jones type of probabilistic theory

[5], the validity of which depends on pairwise independence of terms in both relevant and nonrelevant docu- ments. However, it is clear, if only from the results in this paper, that mutual dependence does not necessarily

lead to poor results. There are many variables involved. In our rather limited experiments most of the initial feedback searches were done under the conditions of the first row of Table 2, that is with terms from title, concepts, narrative

and description (there were a few runs using title and

concepts only, but the results for most topics were not good); and weighting function BMll with termweights

given by equation 6 with large ^3 (1000). This gave

nearly the best precision at 5 and 30 documents of any

of our results. The number of feedback documents was

constant across topics and was varied between 10 and

50. For the final search, terms were always weighted

with BMll, but several values of ^3 were tried (including zero). Some runs used topic terms c«ily and some used expansion terms as well. There was one run omitting narrative and description terms from the final search, but it was not among the very best and is not

reported in the table. The number of terms in the final search was varied from 10 upwards, terms being selected as usual in descending order of iermweight x Some

evaluations were done using frozen ranks, in case the

initial searches tended to give better low precision, but

this turned out not to be the case. A few of the results are summarised in Table 6 . They

include results which appear better than the best otherwise obtained, but the difference is small, and these runs have not yet been repeated on the other topic sets. A qtf weight component is still needed (compare rows

2 and 14 of the table). The number of feedback documents is not critical. Speeding searching by using only

the top 10 or 20 terms is detrimental.

It is interesting that results do not seem to be very

greatly affected by the precision of the feedback set. Looking at the individual topics in the run represented

by the top row of Table 6, 25 did better than in the

feedback run, 18 did worse and the remainder about the same. Restricting to the 20 topics where the precision at 30 in the feedback set was below 0.5, the corresponding

figures are 7, 10 and 3.

29
Taking advantage of the very full topic statements to derive query term frequency weights gives another substantial improvement in the automatic ad-hoc results. Comparing the top row of Table 2 with the top row

of Table 1, there is a 20% increase in average precision.

The "noise" effect of the narrative and description fields

is far more than outweighed by the information they

give about the relative importance of terms (compare

the "TCND" row of Table 1 with the top row of Table

It remains to be discovered how well these new models perform in searching other types of database. Term

frequency and document length components may not be

very useful in searching brief records with controlled in- dexing, but one would expect these models to do well on abstracts. It is also rare to have query statements which are as full as the TIPSTER ones, so there are many situations in which a qif component would have

little or no effect.

7.2 Routing

Our results here (Table 4) were relatively good, and fur- ther improved when re-run with BMll. However, the

TREC routing scenario is perhaps not particularly realistic, given the large amount of relevance information,

which we made full use of as the sole source of query

terms. In addition, the best of our runs depended on

a long series of retrospective trials in which the number of query terms was varied. In a real-world situation one would have to cope with the early stages when there

would be few documents and little relevance information

(initially none at all). It would be necessary to develop a term selection and weighting procedure which was ca- pable of progressing smoothly from a minimum of prior

information up to a TREC-type situation. It may be

possible to come up with a decision procedure for term

selection using something similar to the selection value

w^^^ X Perhaps a future TREC could include some more restrictive routing emulations.

7.3 Interactive ad-hoc searching

The result of this trial was disappointing except on precision at 100 documents (Table 5), scarcely better than

the official automatic ad-hoc run. On three topics it gave the best result of any of our runs, and two more

were good, but the remaining 45 ranged from poor to abysmal. Little analysis has yet been done. For some

topics it is clear that the search never got off the ground

because the searcher was unable to find enough relevant documents to provide reliable feedback information, but

the mean number found per topic was ten, which should

have been enough to give reasonable results (cf Table

6, where ten feedback documents performs quite well).

Currently, there are discussions towards a more realistic

set of rules for interactive searching for TREC-3, and we hope to develop a better procedure and interface.

7.4 Prospects

Paragraphs

When searching full text collections one often does not want to search, or even necessarily to retrieve, complete

documents. Our new probabilistic models do not apply

to documents where the verbosity hypothesis does not apply (Section 2.3). Some of the TREC-2 participants

searched "paragraphs" rather than documents, and this

is clearly right, provided a sensible division procedure

can be achieved. We made some progress towards de- veloping a "paragraph" database model for the Okapi

system, but there has not been time to implement it. Further work then needs to be done on methods of deriving the retrieval value of a document from the retrieval value of its constituent paragraphs.

Parameter estimation

Work is in progress on methods of using logistic regres- sion or similar techniques to estimate the parameters

for the new models.

Derivation and use of phrases and term

proximity

A few results are reported in Table 3. They are not

particularly encouraging. There is probably scope for further experiments in this area, not only on tuples of

adjacent words but also on Keen-type [9] weighting of

query term clusters in retrieved documents.

References

[1] D.K. Barman (Ed.), The First Text REtneval

Conference (TREC-1). Gaithersburg, MD: NIST,

1993.

[2] Robertson S.E. et al. Okapi at TREC. In: [1] (pp.21-30).

[3] Walker, S. and Hancock-Beaulieu, M. Okapi at City: an evaluation facility for interactive IR. London: British Library, 1991. (British Library Research Report 6056.)

[4] Hancock-Beaulieu, M.M. and Walker, S. An eval- uation of automatic query expansion in an online

library catalogue. Journal of Documentation, ^S, Dec. 1992, 406-421.

[5] Robertson, S.E. and Sparck Jones, K. Relevance

weighting of search terms. Journal of the American

Society for Information Science, 27, 1976, 129-146.

30
[6] Cooper, W. ei al. Probabilistic retrieval in the TIPSTER collection: an application of staged logistic

regression. In: [1] (pp. 73-88).

[7] Harter, S.P. A probabilistic approach to automatic

keyword indexing. Journal of the American Society

for Information Science, 26, 197-206 and 280-289.

[8] Robertson, S.E, Van Rijsbergen, C.J. & Porter,

M.F. Probabilistic models of indexing and searching. In Oddy, R.N. et al. (Eds.), Information

Retrieval Research (pp. 35-56). London: Butter- worths, 1981.

[9] Keen, E.M. The use of term position devices in ranked output experiments. Journal of Documentation, 47, 1991, 1-22.

[10] Harman, D. Relevance feedback revisited. In: SI- GIR 92. Proceedings of the 15th International Conference on Research and Development in Information iZefnewa/ (pp. 280-289). ACM Press, 1992.

[11] Robertson, S.E. On Term Selection for Query Expansion. Journal of Documentation, 46, 1990, 359-

364. A 2-Poisson model with

document length component

Basic ideas

The basic weighting function used is that developed in [8], and may be expressed as follows:

w{x) = log

P{x\R)P{0\R)

P{x\R)P{q\R) (8)

where

X is a vector of information about the document;

0 is a reference vector representing a zero-weighted

document;

R and R are relevance and non-relevance respectively.

For example, each component of x may represent the pres- ence/absence of a query term in the document (or, as in the

case of formula 2 in the main text, its document frequency);

0 would then be the "natural" zero vector representing all query terms absent. In this formulation, independence as- sumptions lead to the decomposition of w into additive components such as individual term weights.

A document length may be added as a component of x; however, document length does not so obviously have a "natural" zero (an actual document of zero length is a pathological case). Instead, we may use the average length of a doc- ument for reference; thus we would expect to get a formula

in which the document length component disappears for a document of average length, but not for other lengths.

Suppose, then, that the average length of a document is A. The weighting formula becomes:

w{x, d) = log

P((x, d)\R)Pao,A)\R)

P{ix,d)\R)P(iq,A)\R)

where d is document length, and x represents aU other in- formation about the document. This may be decomposed as

follows: w{-x,d) = w(x,d)i + w(x,d)2 (9) where

P(X\(R.d))PiO\(R,d)) w{x, d)i = log

i'(X|(R,d))P(0|(il,d))

and

w(x dh - loe

P(( Q.d)\R)P((Q,A)\R) W[X,a)2 - log

p((0,d)|fi)P((0,A)|R)

These two components are discussed further below.

Hypotheses

As indicated in the main text, one may imagine different reasons why documents should vary in length. The two hypotheses given there ("scope" and "verbosity" hypotheses)

may be regarded as opposite poles of explanation. The ar- guments below are based on the Verbosity hypothesis only.

The Verbosity hypothesis would imply that document

properties such as relevance and eUteness can be regarded as independent of document length; given eliteness for a term,

however, the number of occurrences of that term would de- pend on document length. In particular, if we assume that

the two Poisson parameters for a given term, A and fi, are appropriate for documents of average length, then the number of occurrences of the term in documents of length d wOl

be 2- Poisson with means Xd/A and fid/ A.

Second component

The second component of equation 9 is PmR,d))PiO\(R,A)) P{d\R) P{A\R)

w(x, d)2 = log —— — + log —

P{q\(R,d))P(0\(R,A)) P{d\R)P{A\R)

Under the Verbosity hypothesis, the second part of this formula is zero. Making the usual term-independence as- sumptions, the first part may be decomposed into a sum of components for each query term, thus:

(p'e-^^/^ + (1 - p')e-^-^/^)(g'e-^ + (1 - g')e-M)

wit. d)o — iog : ^ ' ^ (g'e-^'i/^ -f- (1 - g')e-'^<^/^)(p'e-^ -I- (1 -pOe-*^)

(10) where < is a query term and p', q , A and fi are as in formula

2. Note that there is a component for each query term,

whether or not the term is in the document.

For almost all normal query terms (i.e. for any terms that

are not actually detrimental to the query), we can assume

that p > q and A > /i. In this case, formula 10 can be

shown to be monotonic decreasing with d, from a maximum

as d —* 0, through zero when d = A, and to a minimum as d —>• oo. As indicated, there is one such factor for each of the nq query terms. Once again, we can devise a very much simpler function

which approximates to this behaviour; this is the justifica- tion for formula 5 in the main text.

31
First component >

Expanding the first component of 9 on the basis of term

independence assumptions, and also making the assumption

that eliteness is independent of document length (on the

basis of the Verbosity hypothesis), we can obtain a formula

for the weight of a term t which occurs tf times. This formula

is sinular to equation 2 in the main text, except that A and

H are replaced by Ad/A and fid/A. The factors d/A in components such as A*-'^ cancel out, leaving only the factors of the form e-^'^'^. Analysis of the behaviour of this function with varying tf and c? is a little complex. The simple function used for the experiments (formula 4) exhibits some of the correct properties, but not all. In particular, the maximum value obtained

as d —* 0 should be strongly dependent on tf ; formula 4 does

not have this property.

B Extracts from a searcher's

notes

Choice of search terms

Suitable words and phrases occurring in title, description,

narrative, concept and definition fields were underhned

often this provided more than enough material to begin

with. Sometimes they were supplemented by extra words,

e.g. for a query on international terrorism I added "negotiate", "hostage", "hijack", "sabotage", "violence", "propaganda", as well as the names of known terrorist groups likely to fit the US bias of the exercise.

I did not look at reference books or other on-line databases, and tended to avoid very specific terms like proper names from the query descriptions, as 1 found they

could lead the search astray. For instance, the 1986 Immigration Law was also known as the Simpson-Mazzoh Act,

but the name Mazzoli also turned up in accounts of other

pieces of legislation, so it was better to use a combina.tion of

"real" words about this topic. In some queries, it was necessary to translate an abstract concept, e.g. "actual or alleged private sector eco- nomic consequences of international terrorism" into words

which might actually occur in documents, e.g. "damage",

"insurance claims", "bankruptcy", etc. For this purpose

the use of a genered (rather than domain-specific) thesaurus might be a useful adjunct to the system.

Like the other participants I was surprised at the contents

of the stop- word Hst, e.g. "talks", "recent", "people", "new",

but not "these"! However it was usually possible to find synonyms for stop-words and their absence was not seriously

detrimental to any query.

Grouping of terms, use of operators

Given the complexity of the queries, it was obviously nec- essary to build them up from smaller units. My original intention was to identify individual facets and create sets of

single words representing each, then put them together to form the whole query. [. . . ] For example, for a query about

the prevention of nuclear proliferation I had a set of "nuclear" words (reprocessing, piutonium, etc.), a set of "control" words (control, monitor, safeguards, etc.) and sets of words for countries (argentina, brazil, iraq, etc.) suspected

of violating international regulations on this point. This

proved a bad strategy—the large sets (whether ORed or BMed'^ together) had low weightings because of their collec- tively high frequencies, and the final query was very diifuse. A more successful approach was to buUd several

small, high-weighted sets using phrases with OP=ADJ or OP=SAMES[entence] (e.g. economic trends, gross national product, standard of hving, growth rate, productivity gains),

and then to BM them together, perhaps with a few extra

singletons (e.g. decline, slump, recession). Because of the TREC guidelines, I didn't look at any documents for the small sets as 1 went along, although under normal circumstances I would have done so. Our initial instructions were to use default best-matching

if at all possible, rather than explicit operators. As al- ready suggested, ADJ and SAMES were an absolute necessity given the length of documents to be searched, but AND

and OR were generally avoided—on the occasions when I tried AND (out of desperation) it was not particularly useful. For one query where 1 thought it might be necessary

(to restrict a search to documents about the US economy)

it luckily proved superfluous because of the biased nature of the database, indeed it would have made the results worse as the US context of these documents was implied rather than

stated.

Viewing results, relevance feedback

Normally I looked at about the top 5-10 records from the

first fuU query. If 40% or more seemed relevant, the query was considered to be fairly satisfactory and I went on down

the list trying to accumulate a dozen or so records for the ex- traction phase. As . . . noted by other participants, there was

a conflict between judging a record relevant because it fitted the query, and because it was likely to yield useful new terms

for the next phase. On the one hand were the "newsbyte"

type of documents containing one clearly relevant paragraph

amidst a great deal of potential noise, and on the other the documents which were in the right area, contained all the

right words, but failed the more abstract exclusion conditions of the query. 1 tried to judge on query relevance, but

erred on the side of permissiveness for documents containing

the right sort of terms. The competition conditions discouraged a really thorough

exploration of possibilities when a query was not initially successful. In one very bad case, having seen more than 20

irrelevant records and knowing that they would appear at the head of my output Ust, I felt that the query would show

up badly in the [results] anyway and that it was not worth

exploring further, as 1 might had there been a real question

to answer.

''BM = "best match"; the default weighted set combination

operation was BM15 (see Section 2.6)

32
Extracting new terms

I tried to get at least six relevant documents for the extraction phase, and usually managed a few more. As already

noted, sets generated by term extraction contain oidy sin- gle words, so before looking at the new records I sometimes

added in a few phrases to this set, either important ones from

the original query or others which had occurred in relevant documents. The extracted sets of terms tended to be larger

than the original query and certainly included items which

a human searcher (at least one unfamiliar with this genre of

literature) would not have thought of. It was amusing, for instance, to see "topdrawer" and "topnotch" (epithets for companies) extracted from documents about investment in biotechnology, and "leftist" (an invariable collocate for Sandanista) pulled out of documents about Nicaraguan peace

talks. Some material for socio-linguistic analysis here! My impression ... is that where the original document set from which terms were extracted was fairly coherent, the derived set [from query expansion] also had a high proportion

of relevant documents. Not surprisingly, where I had scraped

the barrel and tried several different routes to a few relevant documents, extraction produced equally miscellaneous and

disappointing results. Normally I went through two or three cycles of selec- tion/extraction, but looking at fewer records each time. The

set of extracted terms did not seem to change materially

from one cycle to the next, and I would have expected the

final result file reflected the query quite well even though the phrases had been lost.

Conclusion

In spite of the frustrations of this exercise, I found it a more

interesting retrieval task than normal bibliographic searching, mainly because it was possible to see the full documents

to gauge the success of the query, and use a broader range of natural-language skills to dream up potentially useful search

terms.

Acknowledgments

We are most grateful to the British Library Research

k. Development Department and to DARPA/NIST for their financial support of this work. Our advisers have

been unstintingly helpful. We blame the system, not our

panel of searchers, for the poor results of the interactive

trial. Above all, we wish to thank Donna Harman of

NIST for her outstandingly efficient and courteous or- ganisation and management of the TREC projects.

33
Table 5: Manual searches with feedback

Run AveP P5 P30 PlOO RP Rcl

% of tops where

AveP > median

Official {BM15)

Re-run (BMll)

0.232 0.492 0.468 0.400 0.297 0.591

0.247 0.480 0.477 0.411 0.315 0.607

40

48

Database: disks 1 and 2. Topics: 101-150

Table 6: Some results from query modification

#fbk

docs

Term

source k3 Terms AveP P5 P30 PlOO RP Rcl

% of tops where

AveP > median

50 TCNDdoc 8 all 0.369 0.660 0.591 0.487 0.408 0.754 92

30 TCNDdoc 8 all 0.368 0.668 0.585 0.486 0.407 0.749 88

10 TCNDdoc 8 all 0.363 0.668 0.584 0.485 0.400 0.748 88

50 TCNDdoc 9 all 0.360 0.624 0.573 0.482 0.399 0.748 88

50 TCNDdoc 9 top 40 0.354 0.620 0.573 0.478 0.394 0.741 86

50 TCNDdoc 9 top 30 0.353 0.632 0.567 0.480 0.395 0.742 88

30 TCNDdoc 8 top 30 0.360 0.676 0.577 0.479 0.402 0.741 88

30 TCNDdoc 8 top 20 0.348 0.636 0.571 0.474 0.392 0.734 82

30 TCNDdoc 8 top 10 0.318 0.604 0.537 0.449 0.366 0.702 78

50 TCND 8 all 0.364 0.636 0.573 0.487 0.406 0.755 92

30 TCND 8 all 0.362 0.644 0.573 0.484 0.408 0.749 90

10 TCND 8 all 0.363 0.640 0.574 0.481 0.406 0.754 88

30 TCND 0 all 0.334 0.652 0.559 0.458 0.374 0.711 80

30 TCNDdoc 0 all 0.310 0.645 0.546 0.448 0.359 0.675 66

Initial feedback run for comparison (top row of Table 2)

None TCND large all 0.360 0.652 0.569 0.479 0.401 0.754 92

Retrospect ive run using all known relevant documents to reweight the topic terms

Variable TCND 0 all 0.371 0.708 0.600 0.497 0.408 0.758 92

Database: disks 1 and 2. Topics: 101-150

34
Combining Evidence for Information Retrieval

NJ. Belkin, P. Kantor, C. Cool, R. Quatrain

School of Communication, Information & Library Studies

Rutgers University

New Brunswick, NJ 08903 USA

[belkin/k:antorp/ccool/quatrain]@cs.rutgers.edu

Abstract

This study investigated the effect on retrieval performance

of two methods of combination of multiple representations

of TREC topics. Five separate Boolean queries for each of

the 50 TREC routing topics and 25 of the TREC ad hoc

topics were generated by 75 experienced online searchers. Using the INQUERY retrieval system, these queries were

both combined into single queries, and used to produce five separate retrieval results, for each topic. In the former case,

results indicate that progressive combination of queries

leads to progressively improving retrieval performance, significantly better than that of single queries, and at least as good as the best individual single query formulations. In the latter case, data fusion of the ranked lists also led to per- formance better than that of any single list.

1. Introduction

The general goal of our project in the TREC-2 program was to investigate the effect of making use of several differ- ent formulations of a single information problem, on in- formation retrieval (IR) system performance. The basis for

this work lies in both theory and empirical evidence. From

the empirical point of view, it has been noted for some

time, that different representations of the same information

problem retrieve sets (or ranked lists) of documents which

contain different relevant, as v/ell as non-relevant documents

(see, e.g. McGill, KoU & Norreault, 1979; Saracevic &. Kantor, 1988). There is some implication from this evi- dence (made explicit by Saracevic and Kantor, 1988), that taking account of the different results of the different formulations, could lead to retrieval performance that is better than that of any of the individual query formulations. From

the theoretical point of view, IR can be considered as a problem of inference (see, e.g. van Rijsbergen, 1986). That

is, IR is concerned with estimating, given available evi- dence about such things as information problems and documents (or in general, retrievable information objects), the likelihood (or probability, or degree) of relevance of a document to the information problem. From this point of

view, different query formulations constitute different sources of evidence which could be used to infer the probable relevance of a document to an information problem, and

it is thus reasonable to consider ways in which lo use (i.e. combine) these sources of evidence in the inference process. These ideas are general to any source of evidence which

might be used for IR, such as the evidence of different re- trieval techniques, or different document representation

techniques, or, in general, different IR systems. One aspect

of our project uses the example of different query formulations as a simulation of the general problem of combination of evidence from different systems.

An additional argument is available for the special case of different query representations. That is, if we consider an

information problem to be a complex, and in general diffi- cult-to-specify entity (see, e.g. Taylor, 1968; Belkin, Oddy

& Brooks, 1982), then we might conclude that each differ- ent representation, derived from some statement by the user,

is a different interpretation of the user's underlying information problem, highly unlikely to be like anyone else's (or

any other system's) interpretation. Given the empirical evi- dence, whether any one such interpretation is 'better' than

another seems moot. However, we might say that each captures some different, yet pertinent aspect of the user's underlying problem; or, that those aspects of the different inter- pretations which are common to them all (or more than one) reflect some 'core' aspect of the problem. Although

techniques for making use of the different interpretations

might vary according to which of these two views one

takes, the general position suggests that it will always be a good idea to take advantage of as many such interpretations as possible. For this case, we therefore consider the issue of combination of different query representations within the 'same' IR system.

Our project, thus, considers the problem of inference in ER at two levels of analysis. The first level, as introduced

by Turtle & Croft (1991), asks about the effect of evidence

obtained when two or more formal query statements are produced for the same information problem. The second

level, which is simulated in this study, asks about combination of evidence provided by two or more distinct sys- tems, ranking the same set of documents in response to the same problem. To distinguish th^se two levels, and in keeping with earlier discussions of the issues involved, we

henceforth refer to the combination of query statements as "query combination", and we refer to the combination of ev- idence from differing systems as "data fiision". Others have

also addressed various aspects of this general question.

Apart from those already cited, we mention in particular the work of Fox and his colleagues (Fox et al., 1993; Fox and

Shaw, this volume), and that of Belkin, et al. (1993).

These studies in fact address precisely the question of query

combination, the Belkin et al. work being a direct precursor

to this, and the Fox et al. studies using different query for- mulation, combination and retrieval techniques, but with very similar results. Why ought either of these two methods work in the IR

situation? The central idea is that either the specific inter- nal score, assigned to a document for a query, or the rank of

35
a document in the list produced for a query, represents in- formation about the relevance of the document to the query. For Boolean retrieval, we may address this question with

concepts of signal detection. In this framework, there are two conditional probabilities. The probability that a rele- vant document is retrieved by system S is d^. The probability that a not relevant document is retrieved is fg. If two

systems (or formulations) are independent, the posterior rel- evance odds are increased by the product did2/fif2- In actual application (Saracevic and Kantor, 1988), improvements are not this large, suggesting either the existence of an effective base of not-relevant documents, or some effect of interdependence. It can be shown that if several query

formulations are drawn from a normal distribution centered

at the optimal query formulation, then some fraction of the time, the simple average of these formulations will be

closer to the optimum than even the best of them. An even

larger fraction of the time, there will be an optimum linear combination which is more nearly optimal than any of the cases from which it is formed (Kantor, 1993).

The existence of such models explains why we might

expect combination of evidence, or data fusion, to work for the case of several query formulations, as, for instance, in the INQUERY retrieval system (Turtle & Croft, 1991).

But these models do not predict that these techniques must

work. The investigation of whether they do work, is the

subject of this paper.

Specifically, we investigate whether data fusion methods will produce better performance than any single method;

and, whether combination of query formulations does better than the best individual query formulations, and whether

progressive combination of query formulations leads to progressively better IR performance. For each of these

questions, we also address the issue of what methods to use

in the combination of evidence.

In this paper, we do not discuss the "official" results which we submitted to TREC-2, except in passing. The

reason for this is that we are not so much interested in the absolute performance of the techniques which we use, as in their performance relative to one another. What we are most concerned with is what happens to retrieval perfor- mance as we combine evidence; if we find that combining

evidence in specific ways leads to improvements over our

starting point of non-combination, then we can begin to investigate how to optimize starting points, as well as rules for combination.

The general plan of our study was as follows. We collected, from experienced online searchers, five different query formulations for each of the 50 routing topics and for 25 of the ad hoc topics. These query formulations were

then put to the INQUERY retrieval system (made available

to us by the University of Massachusetts), both as single

queries, and as combinations of queries for each topic. The

combinations were studied at various levels, with the five- fold combination for each set being reported as "official" TREC-2 results for query combination. The five retrieved

lists for the ad hoc topics were merged, and reported as

"official" TREC-2 results for data fusion.

2. Methods

2.1 Query Formulation Procedures

The query formulations used in this study were generated by volunteer online searchers, all of whom were experienced users of large bibliographic retrieval systems. In order to obtain the multiple query representations, we asked

five different searchers to generate Boolean search state- ments for each of the TREC topics in our analysis. We

asked each of our volunteer searchers to generate a query

formulation for five different topics, resulting in five inde- pendently generated query formulations for each topic. After formulating each query, searchers were asked to answer

four questions about the process: how long it took to for- mulate the query; how related the topic was to their normal

searches; how easy it was for them to formulate the query;

and, the extent to which they had enough information to construct the query. A total of 75 searchers participated in our study; 50 for the routing topics, and 25 for the ad hoc

topics. In addition to the questionnaire items mentioned

above, the ad hoc searchers were also asked how many years of online searching experience they had. Searchers for the routing queries were not asked this question. See the Appendix for a sample response sheet. Our study is based on analysis of the entire set of 50

routing topics, and a selected sample of 25 ad hoc topics.

The sample was stratified according to the domain of the

topic, in an effort to represent the distribution of domains

in the entire set of ad hoc topics. In our experiments, we used the INQUERY retrieval en- gine (version 1.5), developed at the University of Massachusetts (Turtle & Croft, 1991). INQUERY is a probabilistic inference network-based system, which is based

upon the idea of combining multiple sources of evidence in order to plausibly infer the relevance of a document to a query. The underlying formalism is that of a Bayesian

probabilistic inference network (Pearl, 1988), which pro- vides strict rules for how to combine sources of evidence.

Turtle and Croft (1991) give a detailed description of the model and its implementation; a more general description

is available in Belkin and Croft (1992). Here, we note a few characteristics of the system which are germane to the project at hand.

First, INQUERY provides a natural means for combination of multiple query formulations, as a function of its design. Second, it incorporates a large set of operators

which allow, in addition to sophisticated natural language

query formulations, complex Boolean formulations. The

Boolean operators in INQUERY are not strict, however,

which allows ranking of output, and also leads to significantly better performance than strict Boolean retrieval (Turtle and Croft, 1991). See the paper by Croft in this volume for more detail on INQUERY.

2.2 Query Combination Experiments

Each of the Boolean query formulations produced by

our searchers was translated into INQUERY syntax. Two

methods of query combination were then used in our study,

each specific to the TREC-2 tasks of responding to ad hoc

36
and routing topics. The first, which we label "combl" was

applied to the ad hoc topics. In this procedure, we simply

combine the five query formulations for each topic directly, into one query, using the INQUERY "unweighted sum" operator. This query is then used as the search statement in our experiments. In the ad hoc search environment, we

cannot expect to have relevance judgments, and so we can do no more than simple combination.

The second combinatorial procedure, called "combx", was used for the routing topics. Here, we did a separate search for each separate query formulation for all 50 topics, on the training set supplied from the TREC-1 data. From

these results, we used tfie average 1 1-point precision (in the

"official" results reported at TREC-2; precision at 100 documents for the "unofficial results" reported in this paper) of

each query formulation as a weight for that formulation in the combination of all five formulations for each topic.

For this, we used INQUERY's "weighted sum" operator.

This procedure corresponds to constructing a simple combined query, learning something about how that query's

components perform on the current database, and taking ac- count of that evidence to modify the query formulation for searching the next database. These methods of combining queries give us a very

straightforward way to test our hypotheses about the effec- tiveness of multiple sources of evidence. For our experiments (as opposed to the results which were submitted to TREC-2, which were just the combl and combx results as described above), we divided the query formulations for both

ad hoc and routing topics, into five different groups. In each group, each topic was represented by one query, and no

searcher was represented more than once in any one group. This distribution was meant to control for possible searcher

effects. We then did runs for each single group, and for each combination of groups, for both ad hoc and routing

topics. With these data, we were able to compare retrieval performance of different levels of query combination, and to compare retrieval performance of combined queries with un- combined.

2.3 Data Fusion Experiments

Data fusion was accomplished by a list-merging

method which is the natural extension of a 3-out-of-5 data

fusion logic in the binary case. The basic data used was the

five lists of documents retrieved by the five different query

formulations for each topic. Every document has some

rank in each of the five lists being joined together. An ef- fective rank is calculated by taking the third highest of the

five ranks which the document has. This has the same ef- fect as moving a threshold along the list of effective ranks,

and including a document in the output when it has ap- peared on three of the lists. Since there are five scores all together, this can also be thought of as a median rule. In practice, to maintain consistency with other parts of our work, we did not calculate the rank of every document,

but worked with the lists of the top 1000 documents pro- duced in response to each query formulation. This meant

that some documents would appear on all five of the lists, others on just four, or three, or even fewer. Of course, the

whole logic of data fusion suggests that those which appear on more lists are more likely to be relevant. We implemented this, in fact, by forming a combined sort key consisting of (10-degeneracy, 3-rd rank). The degeneracy is the number of lists on which a specific document appears in the top 1000. We used a lexicographic sort, so that all items

with degeneracy 5 appeared before any items with degeneracy 4, and so on. Within a given degeneracy, items with

lower values for the 3rd rank were ranked first.

3. Results

3.1 Caveats

The results presented in this paper differ in several ways

from those submitted as "official" results to TREC-2,

which are published at the end of this volume. According

to our experimental design, there are five independently pro- duced query formulations for each of the TREC topics.

However, due to uneven return rate among our searchers, we

were missing one searcher's set of queries for the ad hoc

topics, and three searchers' sets of queries for the routing

topics, when we did the "official" runs. Consequently, in the official results, five ad hoc topics and fifteen routing

topics are represented by four searches, rather than five. However, we were subsequently able to obtain substitute searchers, and so for the "unofficial" results presented and

discussed in this paper, we have the full complement of 75

searchers and five query formulations per topic.

We were unable to report the data fusion results for routing topxcs for the official results, because of time constraints. We have subsequently been able to do those runs, and report them here as unofficial results. We also caution that one query for one of our ad hoc

topics is known to have a syntactic error which resulted in very poor performance for that single query, and for all un- weighted combinations of queries in which it was present.

Therefore, some of our comparative results in the ad hoc

case may be slightly incorrect.

3.2 General Results

Because our analyses of ad hoc topics are based on a

subset of the total sample, we here consider questions of the sample representativeness. As explained above, the sample was originally chosen to represent topic domains. To see if this had introduced some other bias, we compared the distri- bution of our 25 topics along the three dimensions of topics proposed by Harman (this volume). These are: broadness, operationally defined as the total number of relevant documents found for that topic; hardness, operationally de- fined as inverse to the median average precision for that

topic; and, restriction, defined according to linguistic characteristics of the topic. The distribution of the 25 topics in our sample did not differ significantly from the total ad hoc

topic distribution on any of these dimensions, so we feel reasonably confident that we did not select a markedly bi- ased subset of topics.

Tables 1 and 2 present a descriptive profile of the queries and topics in our study, based upon the query formu37
lations, and the searchers' responses to our questionnaire.

Table 1 shows the distribution of numbers of words and operators per query, and also of time required to construct a

query. Table 2 shows the distribution of searchers' attitudes

to the topics, each indicated on a scale of one to five, from

least to most.

Mean Std Dev. Min. Max. N

Operators 9.94 5.72 1.00 44.00 375

Words 19.40 14.63 1.0 145.00 375

Time

(minutes)

11.31 7.48 1.00 40.00 367

Table 1. Characteristics of queries for ad hoc and routing

topics.

Mean Std Dev. Min. Max. N

Familiarity1.81 1.15 1.00 5.00 388

Ease of

construc- tion

2.82 1.11 1.00 5.00 372

Enough

information

3.20 1.11 1.00 5.00 322

Table 2. Characterization of topics by searchers, for rout- ing and ad hoc topics.

Our ad hoc questionnaire also included a question on how many years of experience each searcher had in online

searching. The mean response was 6.8 years. Unfortunately, we do not have these data for the routing searchers. We wished to consider whether there were any relation- ships between the various characteristics of queries and topics and the performance of the queries themselves. For this purpose, we constructed a table in which each separate query formulation (75x5=375) is associated with performance

measures, the characteristics enumerated in tables 1 and 2, and the three topic categories of broadness, hardness and re- striction defined by Harman (this volume). For perfor- mance, we considered using one or more of three measures: average of 11-point precision; precision at 100 documents;

and R-precision (defined by Harman, this volume). Factor

analysis of these three measures showed that a single factor accounts for more than 90% of the variance among them,

so that they represent, in effect, a single aspect or factor of

performance. The average precision was chosen as representative of this factor, and we have used it both in evaluation

of our retrieval results, and in attempting to determine the

effect of the other variables we have considered, on retrieval performance. Since this variate does not exhibit a normal

distribution, logarithmic and logistic transforms were explored. The logistic leads to a most nearly normal distribu- tion of the transformed score, but we can still not say that the transformed variable follows a normal distribution. The results of applying ANOVA to seek a predictor of

p are shown in Table 3. No significant relations appear. Because of the range of values assumed by the variables

Operators, Words and Time, the relation was sought using

regression analysis. Once again, no significant relations were found, and the scatter plots (not included here) make it clear that there is no trend to be found.. Both hardness and

broadness are significantly related to performance. The

former is expected, since the hardness is determined by median average precision; the latter is less obvious.

Analysis of variance for log(p/(l-p))

Independent variable Significance

Familiarity 0. 149

Easiness 0.169

Information 0.907

Table 3. Significance levels of F-tests using ANOVA to seek dependence of the logistically transformed average pre- cision on the searcher's assessments of their query formulation.

The search for relations between average precision and

characteristics of the query formulation, whether provided

by the search, or determined from the query text itself, was

motivated by the results, discussed below, which show that

it is desirable to weight formulations in proportion to their average precision. Thus, if we could find a surrogate for average precision which can be known without evaluating

the retrieved documents, it would be possible to approximate the effective combination on the first pass of a re- trieval operation. This hope is frustrated at this time.

3.3 Query Combination and Data Fusion

Results: Ad hoc Topics

The official results reported to TREC-2 were for the

overall performance of each of two treatments for the ad hoc

topics, and of one treatment for the routing topics. For

those results, we refer the reader to the relevant section of

this volume. Here we report on our further investigations on the effect of combination of queries, and of data fusion, on performance.

Our first investigation in query combination was to see

if combining query formulations has a regular, beneficial effect, as hypothesized. To do this, we generated the five dif- ferent search groups for the ad hoc topics, as described in section 2.2, and did experimental runs on all single query

groups, all 2-way combinations of queries, all 3-way combinations of queries, all 4-way combinations of queries, and

the combination of all 5 query formulations. • The results are presented in Table 4, where it is evident that the average performance increases monotonically as more evidence is added. The increase is strict and significant, as shown in Table 4a, where we display the number of times that each

combination level performed better than each other level. We note that the data fusion results are not significantly

better than any but 1-way combination (that is, average per- formance for single queries), but also that its performance is not significantly different from unweighted 5-way combination.

38
1 -way 2-w3,y 3 -W3.y 4-w3,y 5 - W3.y fiicinn

0. 1441 0.2016 0.2235 0.2361 0.2349 0.2042

0.1571 0.1823 0.2304 0.2225

0.1121 0.2051 0.2102 0.2292

0.1589 0.1951 0.2043 0 2200

0.1378 0.1763 0.2079 0.2166

0.2113 0.2171

0.1727 0.2172

0.1683 0.1873

0.1633 0.2116

0.1885 0.1934

0.1420 0.1864 0.2103 0.2349 0.2042

1-way 2-way 3-way 4-way 5-way fusion 0.2712 0.3002 0.2959 0.2702 0.2350 0.2042

Table 5. For ad hoc topics, mean 11-point precision for best-performing combination of queries for each topic.

1-way 2-way 3-way 4-way 5-way fusion 1-way 6** 7* 13 17 21**

2-way 29** 16.5 20** 22** 24**

3-way 18* 8.5 20. 5** 22** 22**

4-way 12 5** 4.5** 22.5»» 20**

5-way 8 3** 3** 2.5** 15

fusion 4** J ** 3** 5** 10

For example, column two represents the ten possible ways of

choosing two groups of query formulations from the collection of five groups. Each entry is an average over 25 topics.

Table 4. For ad hoc topics, average 11-point precision,

by group, for each combination of queries, and mean aver- age precision for all groups at each level of combination.

**= significant difference at p < .01, sign test * = significant difference at p < .05, sign test Read row with respect to column, e.g. 2-way performed better than 1-way 19 out of 25 times, or 1-way performed better than 2-way 6 out of 25 times

Table 5a. Number of times, for performance of best combinations for ad hoc topics, that one treatment performed

better than another.

1-way 2-way 3-way 4-way 5-way fusion 1-way J ** 3** 3** 3** 5**

2-way 24** 5** 6** 5** 9 3-way 22** 20** 6** 3.5** 11 4-way 22** 19** 29** 3** 12

5-way 22** 20** 21.5** 22** 15

fusion 20** 16 14 13 10

3.4 Adaptive

Topics

Combination: Ad hoc

**= significant difference at p < .01, sign test *= significant difference at p < .05, sign test Read row with respect to column, e.g. 2-way performed better than 1-way 24 out of 25 times, or 1-way performed better than 2-way 1 out of 25 times

Table 4a. Number of times, for average performance of

combinations for ad hoc topics, that one treatment per- formed better than another.

The results presented in Tables 4 and 4a are based on

the average performance for the query formulations in any one set. In Tables 5 and 5a, we present data on perfor- mance, for ad hoc topics, when only the best query formulation, or best combination of query formulations, for each

topic is used. These results are compared with the single 5- way combination (which is the only combination possible

at this level with our data), and widi the fusion results. It

is of some interest to note that the ranking of level of combination is now very much different than that for average

performance, with 2-way and 3-way combination being significantly better than 1-way, 4-way, 5-way and fusion (see

Table 5a).

Finally, to get an overall idea of how query combination in the ad hoc case worked, and to estimate whether tak- ing account of the evidence of search performance could improve subsequent performance, we compared performance of

simple combination of all five query formulations (combl)

with performance when only the best single query formulation for each topic was used (best), with combination of all five query formulations weighted according to the precision

at 100 documents retrieved, of each formulation (comby).

The results, reported in Tables 6 and 6a, show that there is no significant difference between combl and best, but that comby is significantly better than combl. While formation

of comby would not be possible under the conditions of the ad hoc TREC task, these results are of interest because they

simulate the kind of operations that could be implemented

in a fully interactive interface to an IR system.

combl best comby fusion 0.2350 0.2712 0.2819 0.2042

combl = unweighted combination of all queries for each topic best = best performing query for each topic

comby = weighted (by prec.@100 docs) combination of all queries for each topic

Table 6. For ad hoc topics, mean 11-point precision for four treatments.

In reading Tables 6 and 6a, note that the choice referred

to as "best" corresponds exacdy to the choice called "1-way"

in Table 5. However, it does not correspond to any of the

entries in the first column of Table 4. The entries in Table

39
4 refer to combinations based upon the fixed groups. But,

the combination of groups which performs best for, say. Topic 57, need not be the one which performs best for Topic 72. In Tables 6 and 6a, the best possible combination is chosen for each topic individually. Note also that, in the INQUERY system, the "unweighted sum" corre- sponds to a symmetrical assignment of each weight to all formulations.

combl best comby fusion combl 8 4** 15

best 17 9 21**

comby 21** 16 20**

fusion i 10 4** 5**

** = significant difference at p < .01, sign test * = significant difference at p < .05, sign test Read row with respect to column, e.g. comby performed better than combl 21 times , or combl performed better than comby

4 times.

Table 6a. Number of times that one treatment for ad hoc

topics performed better than another.

3.5 Query Combination and Data Fusion

Results: Routing Topics

We ran fiu-ther experiments on the routing queries,

analogous to those we used for the ad hoc queries. Our first set of results shows the progressive effect of unweighted

combination of query formulations, by level of combination, when average performance at each level is considered

(tables 7 and 7a). Again, as for the ad hoc queries (tables 4

and 4a), there is a progressive, significant effect of level of

query combination. For the routing queries, data fusion ap- pears to have a somewhat stronger effect than for ad hoc,

being significantly better than 1-, 2- and 3- way combination. It is of some interest to note that the overall level of

performance for routing topics is much higher than for the ad hoc topics.

1-way 2-way 3 -way 4-way 5 -way fusion

0.1763 0.2311 0.2599 0.2619 0.2807

0.1890 0.2202 0.2503 0.2748

0.1684 0.2258 0.2603 0.2735

0.2025 0.2229 0.2314 0.2512

0.1793 0.2436 0.2415 0.2745

0.2364 0.2471

0.2388 0.2509

0.2160 0.2654

0.2149 0.2642

0.2338 0.2417

0.1831 0.2283 0.2513 0.2672 0.2807 0.2661

Each entry is an average over 50 topics.

Table 7. For routing topics, average 11-point precision,

by group, for each combination of queries, and mean aver- age precision for all groups at each level of combination.

1-way 2-way 3-way 4-way 5-way fusion 1-way 3** 2** 1 ** 2** 8**

2-way 47** 5.5** 6** 5.5** 13**

3-way 48** 44.5** 9** 7** 18*

4-way 49** 44** 41 ** 8** 22.5

5-way 48** 44.5** 43** 42** 28

fusion 42** 37** 32* 27.5 22

** = significant difference at p < .01, sign test * = significant difference at p < .05, sign test Read row with respect to column, e.g. 2-way performed better than 1-way 47 out of 50 times, or 1-way performed better than 2-way 3 out of 50 times

Table 7a. Number of times, for average performance of

combinations for routing topics, that one treatment per- formed better than another.

As for the ad hoc topics, we then compared the results of the best query formulation combinations for each level of

combination, with the unweighted 5-way combination, and

fusion results. As for the ad hoc queries, this gave us quite a different ranking of levels of combination, with 3-way and

2-way combinations being significantly better than all others, and 4-way being significantly better than 5-way and fu- sion (tables 8 and 8a).

1-way 2-way 3-way 4-way 5-way fusion 0.2931 0.3173 0.3199 0.3069 0.2807 0.2661

Table 8. For routing topics, mean 11-point precision for best-performing combination of queries for each topic.

1-way 2-way 3-way 4-way 5-way fusion 1-way 8.5** 13.5** 22 29 36**

2-way 41.5** 20.5 34* 38** 39**

3-way 36.5** 29.5 37** 42** 45**

4-way 28 16* 13** 44** 40**

5-way 21 12** 8** 6** 28

fusion 14** 11** 5** 10** 22

** = significant difference at p < .01, sign test * = significant difference at p < .05, sign test Read row with respect to column, e.g. 2-way performed better than 1-way 41.5 times, or 1-way performed better- than 2-way

8.5 times

Table 8a. Number of times, for performance of best combinations for routing topics, that one treatment performed

better than another.

3.6 Adaptive Combination: Routing

Topics

Finally, we wished to investigate the effectiveness of

progressively taking account of retrieval performance in

40
modification of the query formulation. To do this, we

compared performance of unweighted 5-way query combination (combl) with performance using the best- performing

query formulations in the training database (bestl), the best performing query formulations in the test database (best2), the weighted 5-way query combination using weights from

the training database (combx), the weighted 5-way query

combination using weights from the test database (comby),

and 5-way query combination weighted by the mean of the weights for test and training databases. The weights diat we used were the precision at 100 retrieved documents for each query formulation. In the official results, we used av- erage 11 -point precision. The reason for the change, is that precision at some cutoff level is a realistic measure for the routing task in general, and especially in an operational en- vironment, whereas the average precision is a measure that we cannot realistically expect to have in an operational en- vironment. When we compared the performance of both

weights in the combx formulation, there was no significant difference. The results are presented in tables 9 and 9a, and

show that taking account of subsequent evidence has a positive and significant effect on performance. When reading

Tables 9 and 9a, note that the entries for combl and fusion

have already appeared in Table 7, as "5-way" and "fusion",

respectively. Also, "best2" has already appeared in Table 8, as the best "1-way" combination.

combl bestl best2 combx comby comxy fusion .2807 .2721 .2931 .3012 .3090 .3068 .2661

combl = xmweighted combination of all queries for each topic bestl = best performing query (on training set) for each topic best2 = best performing query (on test set) for each topic

combx = weighted (by prec.@100 docs in training set) combination of all queries for each topic

comby = weighted (by prec.@100 docs in test set) combination

of all queries for each topic

combxy = weighted (by mean of the sum of prec.@100 docs in training and test sets) combination of all queries for each topic

Table 9. For routing topics, mean 11 -point precision for seven treatments.

Table 9a encapsulates all of the key concepts of the

several approaches to combination that we have explored.

We have two approaches which are a priori and symmetric

in their treatment of the query formulations (fus and

combl). As expected, the fusion system, using the least information, performs worse, combl, the symmetric for- mulations does better, although the difference is not statis- tically significant. Both of these methods often perform

better than the best of the individual formulations, and their relations to other combination schemes are (except for the

relation to best2) quite similar. The query that performs

best on the training set (bestl) does not perform significantly better than any of the combination schemes. But

that formulation which performs best on the test set (best2, also called 1-way in Table 8) is significantly better than

bestl and the fusion scheme.

coml bestl best2 comx comy cxy fus coml 29 21 13.5

* * 16* 14.5* * 28

bestl 21 13** 14** 14** 12.5

* * 22.5

best2 29 37** 23 20 23 36** comx 36.5

* * 36** 27 21.5 18** 40**

comy 34* 36** 30 28.5 25.5 36.5

* * cxy 35.5

* * 37.5* * 27 32* 24.5 37**

fus 22 27.5 14** 10** 13.5

* * 13**

** = significant difference at p < .01, sign test * - significant difference at p < .05, sign test Read row with respect to column, e.g. combx performed better than combl 36.5 times, or combl better than combx 13.5 times.

Table 9a. Number of times that one treatment for routing

topics performed better than another.

Of greater interest are the methods representing adaptive

weighting schemes: combx, comby and combxy. Most

significantly, combx, the adaptive weighting formulation,

is better than the symmetrically weighted combination

(combl), the fusion rule, and the best single formulation in a substantial fraction (over 70%) of all cases. The weighting based on the test set (comby) stands also in essentially the same relation to those three other schemes. Finally, the weighting scheme combxy simulates a situation which

might arise in updating or tuning a combination rule after two batches of documents have been retrieved. This is ac- complished by averaging the weights assigned to each for- mulation in the training run, with those assigned based on

the test run. This scheme shows essentially the same profile as combx and comby when compared with the combl,

fusion, bestl and best2 schemes. It performs significantly better than combx, but not significantly better than comby.

4. Discussion

4.1 General Results

As is customary, we begin this section with a general

disclaimer. In this case, we need to point out that all of our

results were obtained with a very specific kind of query- formulation technique and very special kinds of queries, and,

that all of our results were obtained within a very special re- trieval context, the INQUERY system. It is certainly possible that these circumstances strongly affected our results, so that we cannot make widely general claims for them.

On the other hand, the results reported by Fox and Shaw

(this volume), using queries generated in quite different ways, and using a quite different IR system and retrieval technique, are quite similar in general form and trend to

41
ours, although their specific figures are different. So we are

willing to believe that the influence of our experimental si- tuation is probably not enough to invalidate our results at some level of generality.

There are several aspects of our general results which

are of some interest, apart from the issues of query combination and data fusion. One has to do with the lack of any

significant relationship between number of words in a

query, and the performance of a query. It has been at least informally suggested in the IR community, that the retrieval performance of queries increases with the number of

words in the query. There is no support in our data for this hypothesis. Indeed, in our data, there is at least one one- word query, which performed better than all of the other,

multi-word queries for that topic.

It is also of interest that none of the query/searcher

characteristics was related to performance. This may be a

characteristic of our particular data set, but it also suggests

that it will be rather difficult to identify characteristics of

people or topics at this level, which will be predictive of

performance of the query. Although the level of familiarity by the searchers on

the topics was in general rather low, our searchers nevertheless found it not too difficult to formulate queries (mean of

2.82 on a scale of 1 to 5), and felt that they had sufficient information to construct a reasonable query, on the basis of

the topic (mean of 3.2 on a scale 1 of 5). This makes us

think that the queries are likely to be reasonable formulations of the search topics, at least as far as the searchers are concerned. But the range and variability of the numbers of

words and numbers of operators per topic seems to indicate

that the query formulations themselves are rather different (we have not yet compared them for overlap in specific

words, but work on this issue is in progress). These two

results seem to us to confirm our initial idea that each query

formulation is indeed a "different" interpretation of the in- formation problem, and thus to substantiate our general ap- proach.

4.2 Query Combination Results

Our results, for both ad hoc and routing topics, seem

clearly to show that, in general, the more evidence one has,

and uses, in the form of different query formulations, the

better the IR performance is going to be. In particular, ta- bles 4, 6, 7 and 9 support this conclusion, in various re- spects. From the results of tables 6 and 9, we can see that, taking advantage of what one learns about query perfor- mance from one iteration doesn't help a lot, after the first iteration, but on the other hand, it doesn't hurt, either. This

suggests to us that continual modification and reweighting

of the multiple query formulations in a combined query, is likely to be useful in the general routing environment. But

even doing it once, given the initial evidence, seems to help. This also suggests that continuing to add new query

formulations to a combined query will likely help perfor- mance on subsequent runs.

Having said all this, it is worth considering the results

of tables 5 and 8, which showed that picking the best 2-way

or 3 -way combination of query formulations was significantly better than using 4-way or 5-way combinations. On

the face of it, this runs counter to the general result of "the more, the better". However, it is possible that this result is an artifact of our data. For both 2-way and 3 -way combinations, it was possible to choose the best from ten different combinations. Because we had only five different query

formulations for each topic, we had smaller pools from

which to choose, for both single query formulations, and

for the 4-way and 5-way combinations. This issue needs

further investigation.

4.3 Data Fusion Results

There are several points to be made with regard to the median-fusion scheme as implemented here. First, as may

be expected from general arguments, it sometimes performs

better than the best of the lists which are joined by the fu- sion process. Second, it does not perform as well as even

the symmetric (unweighted) combinations made using the

internal scores generated by the INQUERY system. This is expected, since those scores contain more information than

the rankings alone. One can imagine special cases in which

the distribution of scores assigned to a document by several queries is such that the internal combination rule of un- weighted sum does not perform as well, but this has apparently not occurred in the cases studied here. Third, in the application to the routing problem we

have, in fact, operated in a batch mode. For a true routing

situation, it would be necessary to estimate cutoff scores for the several query formulations, corresponding to the cutoff rank on the fused list. For large data sets this can be done

easily. Without this step, it is not possible to make an immediate decision about a newly presented document.

Fourth, the stability induced by using this system was

manifested in the case of the one query for which we dis- covered, too late to make the change, that one of the query

formulations was in error. For this case, all of the "average

combination of evidence formulations" performed more

poorly than the fusion rule. This is because one, or even two disastrously bad query formulations will have little ef- fect on the results of the 3-of-5 fusion rule. Of course, ex- pect for the case of combining all five query formulations,

the best of one, two, three or four query formulations can do well because the one bad formulation will be missing

from the combinations that are best. Finally, the application of data fusion here, at the so- called decision level (that is to say, after the documents

have been ranked according to several rules) is a simulation

for the case to which it should be appUed. Since the specific system that we used permits internal manipulation of

scores, there is no need to delay combination until after the output lists have been formed. But in realistic settings, several distinct systems will have internal operations which

are not compatible, so that, even if it were possible to extract the internal scores, it would not be apparent how to combine them.

42
5. Conclusions ARPA.

In general, we conclude that our initial research questions with respect to query combination have been positively answered. That is, if one has available several differ- ent representations of a single information problem, then it makes sense to use all of them, in combination, in order to improve retrieval performance, rather than to try to identify

and use only the best one. In addition, it is reasonably clear

that progressive and continuous combination of query for- mulations leads to continuing and progressive improvement

of performance. This may extend to progressive modification of query formulations in the routing situation, for in- stance, on the basis of each iteration of retrieval. Nevertheless, some of our results appear anomalous, and in particular we need to address more carefully the issue of how best

to combine query formulations.

As far as our data fusion questions are concerned, we

have clearly demonstrated that doing data fusion is better than using only one query formulation. Although perfor- mance improvement in these experiments was rather low,

for operational settings in which there are multiple systems

with incompatible scores, a data fusion method that works

with the rar^ed outputs, rather than the scores is the precise

method that is needed. In the present study we have shown

how that method can be extended from the case of binary

(set) retrieval to the case of ranked lists. We have shown

that the results are, on the average, better than the results of

the individual formulations. In some cases, they are better than the best of the component formulations. This lends

support to a program of seeking optimal tunings for fusion

of any number of given systems, to achieve results better than any of them alone could provide.

Overall, we find strong support for adaptive weighting

in query combination. This is applicable to both routing,

as shown directly here, and to relevance feedback, which we

have simulated in our application to the ad hoc topics. We

also find strong support for enlarging the set of query representations. This success raises many interesting possibilities. For example, one might systematically explore the k- way combinations to see how they compare to the adaptive

weighting scheme. Or, one might apply the notion of

adaptive weighting to the best of the k-way combinations.

The possibilities for combining these two concepts ex- plodes (of course) combinatorially. We feel that the present

experiments point a way into the forest of possibilities.

6. Acknowledgments

We wish to thank the 75 searchers who so generously

donated their time and effort to this project Without them

this research could not have been done. We also wish to thank Audrey Gorman and Kathy Mrowka, who provided

invaluable assistance on the project in planning, data gathering and input, and Dong Li, who helped with the data

analysis. We owe special thanks to Bruce Croft and Jamie

Callan, not only for permission to use the INQUERY sys- tem for this investigafion, but also for the unstinting support they gave us in using it. This research was performed

with partial funding from a TREC support grant from the

7. References

BELKIN, N.J., COOL, C, CROFT, W.B. & CALLAN,

J.P. (1993). The effect of multiple query representations on

information retrieval performance. In: Proceedings of the 16th International Conference on Research and Development in Information Retrieval (SIGIR '93), Pittsburgh, 1993. New York, ACM: 339-346.

BELKIN, N.J. & CROFT, W.B. (1992) Information filter- ing and information retrieval: Two sides of the same coin?

Communications of the ACM, 35,12: 29-38.

BELKIN, N.J, ODDY, R.N. & BROOKS, H.M. (1982)

ASK for information retrieval. Journal ofDocumentation,

38, 2&3: 61-71, 145-164.

FOX, E.A. et al. (1993) Combining evidence from multi- ple searches. In: D.K Harman, ed.. The First Text REtrieval

Conference (TREC-I). GPO, Washington, D.C.: 319-328.

KANTOR, P. (1993) Vector space models of data combination in information retrieval. Technical Report APlab/TR93-3. New Brunswick, NJ., Rutgers University, School of

Communication, Information & Library Studies.

MCGILL, M., KOLL, M. & NORREAULT, T. (1979)

An evaluation of factors affecting document ranking by in- formation retrieval systems. Syracuse, Syracuse University

School of Information Studies,

SARACEVIC, T. & KANTOR, P. (1988) A study of in- formation seeking and retrieving. III. Searchers, searches,

overlap. Journal oftheASIS, 39,3: 197-216.

TAYLOR, R.S. (1968) Question negotiation and information seeking in libraries. College and Research Libraries,

29: 178-194.

TURTLE, H. & CROFT, W.B. (1991) Evaluation of an

inference network-based retrieval model. ACM

Transactions on Information Systems, 9,3: 187-222.

VAN RIJSBERGEN, C.J. (1986) A new theoretical framework of information retrieval. In: Proceedings of the 1986

International Conference on Research and Development in Information Retrieval (SIGIR '86), Pisa, 1986. New York,

ACM: 194-200.

43
APPENDIX: SEARCHER RESPONSE SHEET

QUERY FORMULATION

TOPIC NUMBER: PACKET NUMBER:

Please formulate a search query for one of the five topics you have been sent, in the space below. Don't

forget to indicate the topic number in the space above. Please read the entire topic description before you

begin your query forumlation. (Use the back of this sheet, if there isn't enough room below)

Please answer the following questions, as they relate to this specific query formulation.

1. About how many minutes did it take (including reading the topic description)? minutes

2. Is this topic related to things you normally search on (please circle one number)?

Not at all Somewhat Very much

3. How easy was it to formulate this query?

Not easy Somewhat Very easy

4. Do you feel you had enough information to construct an effective query?

Too little Adequate Plenty

5. About how many years have you been doing online searching? : years.

44
Automatic Routing and Ad-hoc Retrieval

Using SMART : TREC 2

Chris Buckley* James Allan, and Gerard Salton

Abstract

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in the

TREC 2 environment, performing both rout- ing and ad-hoc experiments. The ad-hoc

work extends our investigations into combining global similarities, giving an overall indication of how a document matches a query, with

local similarities identifying a smaller part of

the document which matches the query. The

performance of the ad-hoc runs is good, but it

is clear we are not yet taking full advantage of

the available local information.

Our routing experiments use conventional

relevance feedback approaches to routing, but

with a much greater degree of query expansion than was done in TREC 1. The length

of a query vector is increased by a factor of

5 to 10 by adding terms found in previously seen relevant documents. This approach improves effectiveness by 30-40% over the original query.

Introduction

For over 30 years, the Smart project at Cornell University has been interested in the analysis, search, and retrieval of heterogeneous

text databases, where the vocabulary is al- lowed to vary widely, and the subject matter

is unrestricted. Such databases may include

newspaper articles, newswire dispatches, text- books, dictionaries, encyclopedias, manuals,

magazine articles, and so on. The usual text analysis and text indexing approaches that are based on the use of thesauruses and other vo- cabulary control devices are difficult to apply

* Department of Computer Science, Cornell University, Ithaca, NY 14853-7501. This study was sup- ported in part by the National Science Foundation under grant IRI 89-15847.

in unrestricted text environments, because the

word meanings are not stable in such circumstances and the interpretation varies depending on context. The applicability of more complex text analysis systems that are beised on

the construction of knowledge bases covering

the detailed structure of particular subject areas, together with inference rules designed to derive relationships between the relevant concepts, is even more questionable in such cases. Complete theories of knowledge representation

do not exist, and it is unclear what concepts,

concept relationships, and inference rules may

be needed to understand particular texts. [11] Accordingly, a text analysis and retrieval component must necessarily be based primarily on a study of the available texts themselves.

Fortunately very large text databases are now

available in machine-readable form, and a substantial amount of information is automatically derivable about the occurrence properties

of words and expressions in natural-language

texts, and about the contexts in which the

words are used. This information can help in determining whether a query and a text are se- mantically homogeneous, that is, whether they

cover similar subject areas. When that is the

case, the text can be retrieved in response to the query.

Automatic Indexing

In the Smart system, the vector-processing

model of retrieval is used to transform both

the available information requests as well as the stored documents into vectors of the form:

Di = {Wii,Wi2, . . . , Wit)

where Di represents a document (or query)

text and Wik is the weight of term Tk in document Di . A weight of zero is used for terms

that are absent from a particular document,

and positive weights characterize terms actu45
ally assigned. The cissumption is that t terms

in all are available for the representation of the

information.

In choosing a term weighting system, low

weights should be assigned to high-frequency

terms that occur in many documents of a col- lection, and high weights to terms that are important in particular documents but unimportant in the remainder of the collection. The

weight of terms that occur rarely in a collec- tion is relatively unimportant, because such

terms contribute little to the needed similarity computation between different texts. A well-known term weighting system fol- lowing that prescription assigns weight Wik to term Tk in query Qi in proportion to the fre- quency of occurrence of the term in Qi , and

in inverse proportion to 'the number of documents to which the term is assigned. [12, 10] Such a weighting system is known as a tfx idf (term frequency times inverse document fre- quency) weighting system. In practice the

query lengths, and hence the number of nonzero term weights assigned to a query, varies

widely. To allow a meaningful final retrieval

similarity, it is convenient to use a length nor- mahzation factor as part of the term weighting

formula. A high-quality term weighting for- mula for Wik , the weight of term Tk in query

Qi is

(log(/ifc) + 1.0)*log(iV/nfc)

Wik — —^===^^^^===^^=^=^=^

\/ELi[(log(/ifc) + 1-0) * log(iV/n,)]2

(1)

where fik is the occurrence frequency of Tk in Qi, N is the collection size, and nk the number of documents with term Tk assigned. The

factor log(N/nk) is an inverse collection fre- quency ( "idf ) factor which decreases as terms

are used widely in a collection, and the denominator in expression (1) is used for weight nor- malization. This particular form will be called

"Itc" weighting within this paper. The weights assigned to terms in documents

are much the same. In practice, for both effec- tiveness and efficiency reasons the idf factor in the documents is dropped. [1] The terms Tk included in a given vector can

in principle represent any entities assigned to a document for content identification. In the

Smart context, such terms are derived by a

text transformation of the following kind:[10]

1. recognize individual text words

2. use a stop list to eliminate unwanted function words

3. perform suffix removal to generate word

stems

4. optionally use term grouping methods

based on statistical word co-occurrence

or word adjacency computations to form term phrases (alternatively syntactic analysis computations can be used)

5. assign term weights to all remaining word

stems and/or phrase stems to form the

term vector for all information items.

Once term vectors are available for all information items, all subsequent processing is based on term vector manipulations.

The fact that the indexing of both documents and queries is completely automatic means that the results obtained are reasonably

collection independent and should be valid across a wide range of collections. No human

expertise in the subject matter is required for either the initial collection creation, or the ac- tual query formulation.

Phrases

The same phrase strategy (and phrases) used

in TREC 1 ([1]) is used for TREC 2. Any

pair of adjacent non-stopwords are regarded

as potential phrases. The final list of phrases

is composed of those pairs of words occurring in 25 or more documents of the initial TREC 1 document set (Dl, TREC 1 initial collection). Phrase weighting is again a hybrid scheme where phrases are weighted with

the same scheme as single terms, except that

normalization of the entire vector is done by

dividing by the length of the single term subvector only. In this way, the similarity cori- tribution of the single terms is independent of

the quantity or quality of the phrases.

Text Similarity Computation

When the text of document Z),- is represented

by a vectors of the form [dn, di2, • .

. , dn) and

query Qj by the vector {qji,qj2, ,qjt), a

similarity (S) computation between the two

items can conveniently be obtained as the in- ner product between corresponding weighted

46
term vector as follows:

t S{Di,Qj) = ^{da*qjk) (2)

ife=i Thus, the similarity between two texts (whether query or document) depends on the

weights of coinciding terms in the two vectors. Information retrieval and text linking sys- tems based on the use of global text similarity measures such as that of expression (2) will be successful when the common terms in the two vectors are in fact used in semantically similar ways. In many cases it may

happen that highly-weighted terms that contribute substantially to the text similarity are semantically distinct. For example, a sound

may be an audible phenomenon, or a body of water. TREC 1 ([!]) demonstrated that local contexts could be used to disambiguate word

senses, for example rejecting documents about

"industrial salts" when given a query about

the "SALT peace treaty". Overall, however,

the improvement in effectiveness due to local matching was minimal in TREC 1 . One reason

for this is the richness of the TREC queries.

Global text matching is almost invariably sufficient for disambiguation. Another reason is the homogeneity of the queries. They deal primarily with two subjects: finance, and science

and technology. Within a single subject area vocabulary is more standardized and ambiguity is therefore minimized.

One other potential reason for the unexpectedly slight improvement is that most of the in- formation from local matches is simply being

thrown away. Local matches are used as a fil- ter to reject documents that do not satisfy a

local criteria: the overall global similarity used

for ranking is changed only by the addition of a constant indicating the local match criteria was satisfied. The positive information that a long document might have a single paragraph

which very closely matched the query is ignored.

For TREC 2, we look at combining global

and local similarities into a single final similarity to be used for ranking purposes. The other focus of our TREC 2 work is tak- ing advantage of the vast quantity of relevance

judgements available for the routing experi- ments. In TREC 1, the relevance information was fragmentary and even occasionally incorrect. It was hard to use this information in a

reasonable fashion. Happily, the results of the

TREC 1 experiments furnished a large number of very good relevance judgements to be

used for TREC 2. Conventional vector-space

feedback methods of query expansion and re- weighting are tuned for the TREC environment in the routing portion of TREC 2.

System Description

The Cornell TREC experiments use the

SMART Information Retrieval System, Version 11, and are run on a dedicated Sun Sparc

2 with 64 Mbytes of memory and 5 Gbytes of

local disk. SMART Version 11 is the latest in a long

line of experimental information retrieval sys- tems, dating back over 30 years, developed under the guidance of G. Salton. Version 11 is a reasonably complete re-write of earlier versions, and was designed and implemented primarily by C. Buckley. The new version is ap- proximately 44,000 lines of C code and documentation.

SMART Version 11 off"ers a basic framework for investigations into the vector space

and related models of information retrieval. Documents are fully automatically indexed,

with each document representation being a

weighted vector of concepts, the weight indi- cating the importance of a concept to that particular document (as described above). The

document representatives are stored on disk as an inverted file. Natural language queries un- dergo the same indexing process. The query

representative vector is then compared with

the indexed document representatives to ar- rive at a similarity (equation (2)), and the documents are then fully ranked by similarity.

Ad-hoc Results

Cornell submitted two runs in the ad-hoc cate- gory. The first, crnlV2, is a very simple vector comparison. The second, cmlL2, makes use

of simplified least squares analysis and a train- ing set to combine global similarity and partwise similajities in a meaningful ratio. Both

systems performed at or above the median in almost all queries, as can be seen in in Table 1. The crnlV2-b run is the same as the official crnlV2 run, but with an error in the experimental procedure corrected (discussed below).

47
Run Best > median < median

crnlV2

crnlL2

crnlV2-b

1 38 11 4 40 6

9 36 5

Table 1: Comparative Ad-hoc results

Global Similarity

The crnlV2 run demonstrates the quality of

results obtainable with simple methods. The

weighting for terms is chosen based upon re- sults from TREC 1. Query terms are weighted

by the formula in equation (1) ("Itc" in Smart's vocabulary). Document terms are weighted using a normalized logarithmic term

frequency ("Inc"):

log fik + 1.0 . , dik = I = (3)

where dik is the weight of term Tk in document

Di, fik is the occurrence frequency of term

Tk in document Di , and / is the total number

of terms in the collection. The denominator

provides normalization of vector length. Note

the absence of the "idf" factor log{N/nk).

Table 2 shows the results of that weighting scheme in crnlV2-b. Regrettably, be- cause of an oversight during the official run

(a misnamed inverted file), the official submitted run crnlV2 did not use the weighting ap- proach described above (and recommended in our TREC 1 report). Instead crnlV2 accidentally used the "idf" factor in both the query

and the document. That mistake caused 10%

loss in retrieval effectiveness (from a recall- precision average of 0.3512 to 0.3163).

Global and Local

Cornell's TREC 1 ad-hoc submission in- creased the similarity measure of a query

and document if some sentence in the query

matched some sentence in the document suf- ficiently well.[l] The result was that any

query/document pair which contained a sen- tence match was retrieved before all that did not have such a match. For TREC 2, we hoped

to find a less restrictive balance between the

global and local similarities. At the same time, we wished to investigate local similarities using parts other than sentences, and to investi- gate combining muHtple local similarities. Our approach is similar to that used in [4]. We built a training collection using the 50

queries from Q2 and the 74,520 documents

from the Wall Street Journal included in D2.

For each of the 3.7 million query/ document

pairs, we calculate the global similarity and some set of local similarity values. The least squares polynomials (LSP) approach devel- oped for [4] are used to find the "ideal" coefficients for the global and local values in the

equation:

sim = a global -|- /?i • locali + /?2 • local2 -|- . .

.

(The LSP functions actually yield a constant

factor which we ignore since it does not affect ranking.)

We consider local values from the following

broad classes:

• Comparing sentences of the query against sentences of the document. In general, we

use a simple "tfxidf" weight without nor- malization, though we experimented with

other weights.

• Comparing paragraphs of the query

against paragraphs of the document. (For

the most part, each section of the query

topic is a separate paragraph.) In this

case, we use the weighting of equation 1 above for the query paragraphs, and try a

variety of weights for the document paragraphs.

• Comparing the query against paragraphs

of the document. We use the same

weighting schemes as above.

We also tried combinations of the above categories: e.g., the best matching paragraph pair

and the best matching sentence pair. See Table 3 for a complete list of local values that were considered.

In all, we tried 72 combinations of local and

global values, using from one local value to 19

different local values.^ The LSP-determined a and /?i's of those values are then applied

to a retrieval run on that same set of queries

and documents. The top performing result in- cludes only a single local value: the best match

^ There were roughly 1.2 million possible combinations; we chose 72 that seemed, based on earlier experiments, Ukely to succeed.

48
Run R-prec Total Rel recall-prec

crnlV2 3640 8018 3163

crnlV2-b, 4053 8256 3512

crnlV2-b, (no not's 4061 8254 3560

crnlL2 3641 8224 3258

crnlL2-b 3922 8379 3538

sentence restricted 3960 8252 3477

Table 2: Ad-hoc results

of the query against the paragraphs of the can- didate document (Ill.a.l from Table 3), with

the query terms weighted 1 iff present, and

the document terms weighted using formula 1 above (that used by the query in the global

similarity).

We then use the global/local values in a se- ries of retrieval runs using the same queries

but against the entire TREC 1 document set (D12). We tried a range of a and /3 values and

use the best values for the official run, crnlL2.

The formula used for crnlL2 is:

sim = 100 • global + 16 local

where "global" is the query/document similarity described above ("Itc-lnc"), and "local" is the top query/paragraph similarity.

It takes roughly 5 hours clock time to de- termine the suggested weighting coefficients,

though multiple combinations of values could

be weighted simultaneously—in one case, we

calculated each of the 48 possible local vari- ables simultaneously. Each of the retrospective runs takes from 60 to 90 minutes to run, depending on its complexity. These runs take an unusually large amount of time (compared

to crnlV2) since they require re-indexing from

scratch a large number of documents. The basic procedure is to discover the top 1750 documents for each query using the global similarity. Then each of those documents is re- indexed, breaking it down into its component

parts (e.g., paragraphs). Then each component part is compared against the query to obtain local similarities.

Other Experiments

The Smart indexing procedures that are used

in our experiments do not analyze the documents or queries for negative terms such as

not. A query which explicitly requests documents "no< about the United Kingdom or Canada" will actually match any document

with those terms. Removing the negative keywords results in insignificant improvement: 16

queries are helped, 16 are hurt, all in only a minor fashion. These results suggest that other

terms in the query were more important for locating the relevant documents.

Earlier experiments with an on-line encyclopedia ([14, 16]) demonstrated that precision can be improved by discarding documents which fail a local context check (cf. [1] where such documents were merely given lower

similarity measures). That approach on the

TREC 2 queries and collection yields almost

exactly the same performance as crnlV2-b

(see "sentence restricted" in Table 2). [1] dis- cusses probable reasons for the limited success of this method.

Analysis of Ad-hoc Results

The results of Table 2 suggest that there is

little advantage to using local values in combination with global matches. From run crnlV2

to run crnlL2 there is negligible improvement,

crnlL2 does retrieve an additional 120-200 rel- evant documents.

The retrospective runs using the Wall Street Journal sub-collection suggested there would

be greater improvement between crnlV2 and

crnlL2 than actually occurred. The most obvious problem is that the definition of a para- graph is sub-collection dependent. Our results were tailored to the WSJ sub-collection and

probably did not apply well to the other subcollections where "paragraphs" might be ex- tremely large.

49
IN d-IHc IVnm

Pairs T

X Qu6ry sentences vs. doc sentences

TT11 Query pcircigraphs vs. doc paragraphs

TTT rOnfirp miprv vci nor n^rj^0TPi'nn<s

VV nicn a Top matching pair

D Non-zero matching pairs

c Pairs where similarity exceeds threshold

Au. All ^\^^ vc Value 1 Similarity (avg)

2 Number of common terms (avg)

3 Top matching term (avg)

4 Count of pairs

Table 3: Local values considered for LSP weighting

(all combinations, choosing one from each category)

Future work

We are currently investigating the use of re- gression analysis to find correlation between

relevance and local similarity values. Using

such analysis will allow the local values to be

selected for cause rather than solely because of

experience and intuition. If successful, it will also provide a collection-independent method

of selecting which local values are useful. Note

that this approach does require a training set of queries and relevance judgements.

We are interested in applying these tech- niques to the TREC collections with a more

useful definition of "paragraph." [17, 2] suggest the possibility of narrowing the search

window to fixed-size pieces, ignoring para- graph boundaries. Hearst's "TextTiling" ap- proach ([7]) is intriguing for the topic-coherent

units of text it produces.

Routing

In this work, routing queries are formed in two distinct phases. In the first phase, concepts which occur often in relevant documents

are added to the original query to expand the

vocabulary used. In the second phase, the

original concepts plus the added concepts are weighted based upon their occurrences in rel- evant and non-relevant documents.

In TREC 1, query expansion was a major

obstacle. It was clear that only very limited

expansion was useful, and indeed the best au- tomatic routing run ([5]) used no expansion at

all. Thus the original plans for TREC 2 rout- ing included extensive investigation into very

selectively adding concepts to queries.

However, as work on TREC 2 progressed

it became obvious that the TREC 1 results were somewhat anomalous. For the routing

approaches used in this work, selectivity of

added terms is not an issue. Rather, the more

terms that are added, the better the result

up to a point of diminishing returns. This re- sult agrees with our experiences on the (small)

feedback test collections that we have worked

with in the past. The original TREC 1 train- ing data for routing was extremely sketchy

and the resulting unusual query expansion re- sults were probably due to the lack of infor- mation about what a representative relevant

document looked like. The basic routing approach chosen is the

feedback approach of Rocchio ([9, 13]). Expressed in vector space terms, the final query

vector is the initial query vector moved to- ward the centroid of the relevant documents,

and away from the centroid of the non-relevant

documents.

Qnew — * Qo\d

+ B * average_wt_in_reLdocs — C * average_wt_nonrel_docs

Terms that end up with negative weights are dropped (less than 3% of terms were dropped

in the most massive query expansion below).

The parameters of Rocchio 's method are the

relative importance of the original query, the

50
relevant documents, and the non-relevant documents (A,B,C above); and then exactly what

terms are to be considered part of the final vector.In TREC 1, a similar approach originally

proposed by Ide was used. [8, 13] That seemed

to work well for the fragmentary relevance in- formation available for TREC 1. TREC 2 has considerably more information available

and of much higher quality—so Rocchio's ap- proach is more appropriate.

The best parameter values to use for Rocchio's algorithm were investigated by splitting

the TREC 1 collection into two parts along

the natural Dl (TREC 1 initial collection) and

D2 (TREC 1 routing collection) lines. Dl

formed the learning set and D2 the evaluation

set for a large number of experimental runs determining these parameters. The original

TREC 1 routing queries (Q2) are expanded

and weighted using Rocchio's algorithm with

the relevance information from Dl. They are then evaluated by running them against D2

and using the known Q2-D2 relevance infor- mation.

Queries are expanded by adding the "best" X single terms and the "best" Y phrases to the original query. We used a simple notion of

"best" for TREC 2: terms that occurred in the

most relevant documents (ties were broken by

considering the highest average weight in the

relevant documents).

There is a core set of 158 runs using differ- ent parameter values for both expansion and

weighting. Table 4 gives the six parameter

possibilities The trends noticeable in this in- vestigatory set of runs are:

1. Overall effectiveness increases strongly as the number of terms added increases, up

until 200 terms at which point it starts to

level off.

2. Phrases are reasonably important (6%

difference) at low single term expansion

numbers, but become less important at higher values (1% difference)

3. As expected, weights in relevant documents are far more important than

weights in non-relevant documents.

The parameters of our official run, crnlRl are: adding X = 300 single terms, adding Y — 50

phrases, importance of original query of A = 8,

importance of weight in relevant documents of

B = 16, importance of weight in non-relevant

documents of C = 4, and relative importance

of phrases at retrieval time of P = 0.5.

Query-by-Query Parameter Estimation

We examined the results for the 158 test rout- ing runs in more detail, query by query. For

each of the 50 queries, we found the best test run. The results (see Table 5) show some in- teresting patterns not brought out by the overall averages. Not surprisingly, the parameters

used for crnlRl are not best for any single

query; they are just a reasonable compromise.

There seem to be two main groups of queries: one in which very limited expansion is useful (even 6 queries where no expansion is preferred); and one in which the more terms are added, the better (23 queries with expansion

of 500 single terms). If massive expansion is useful, in general the original query is less important than the expanded terms: A is much

less than B. There is another separate dis- tinction between those queries where phrases

are useful and those where phrases appear useless: 1 query worked best adding 100 phrases,

6 with 50 added, 2 with 10, 16 using the original phrases only, and 25 using no phrases at

all.If we retrospectively choose the best parameters for each query (something that cannot be

done in practice), then we achieve roughly a 10% improvement. This is substantial enough

to actually try a predictive run, so our sec- ond official run (crnlCl) uses query-by-query

choice of parameter values in a predictive (as

opposed to retrospective) fashion. The values

given in Table 5 were used.

Routing Results

Both crnlRl and crnlCl do extremely well in comparison with other TREC 2 routing runs: Run Best > median < median

crnlRl

crnlCl

7 40 3

5 45 0

Evaluation measures in Table for both the official and some non-official runs show the importance of query expansion. Run 1 is the

base case original query only (Itc weights).

51
X number of single terms to add (possible values 0 to 500)

Y number of phrases to add (0 to 100)

A relative importance of original query (fixed at 8)

B relative importance of average weight in relevant documents (4 to 48)

C relative importance of average weight in non-relevant documents (0 to 16)

P relative importance of phrases in final retrieval as compared to single terms (0, 0.5, or 1.0)

Table 4: Parameters of routing

Just re-weighting the query terms according

to Rocchio's algorithm gives a 7% improvement. Adding a few terms (20 single terms

+ 10 phrases) gives 17% improvement over

the base case, and expanded by 350 (300-f50)

terms results in a 38% improvement.

The official run crnlCl is actually a bit dis- appointing. It only results in a 3% improvement over the crnlRl run, which is not very

significant considering the eflfort required. Few

people are going to keep track of 158 test runs on a per query basis. It may be practical to keep track of 4 or so main query variants, but

then the improvement would probably be less than 3%. We are conducting experiments in this area currently.

An open question is the effectiveness of

varying the feedback approach itself between

queries. Preliminary experiments using Fuhr's

RPI ([3]) weighting schemes in addition to the

Rocchio variants show larger improvements.

In general, RPI (and the other probabilistic

models) perform noticeably better than Rocchio if there is very little query expansion,

though quite a bit worse under massive ex- pansion. We expect that the combination of

RPI for those queries with little expansion and

Rocchio for other queries will work well. One benefit of the crnlCl run not entirely

represented by the evaluation figures is that

retrieval performance is more even. Potential mismatches between feedback method and

query are far less likely. crnlCl does reason- ably on all the queries (above the median sys- tem for every query when compared against

the other systems).

Routing Implementation and

Timing

The original routing queries are automatically

indexed from the query text, and weighted using the "Itc" weighting scheme (equation (1)). Collection frequency information used for the

idf factors is gathered from D12 documents

only. Relevance information about potential

query terms is gathered and stored on a per

query basis. For each query, statistics (including relevant and non-relevant frequency and

total "Itc" weights) are kept about the 1000

most frequently occurring terms in the D12

relevant documents. For TREC 2, this is done

by a batch run taking about 90 CPU minutes.

In practice, this would be done incrementally

as each document was compared to the query

and judged. The statistics amounted to about

40,000 bytes per query. Using these statistics, and the decided upon

parameters for the feedback process (A, B,

etc.), actual construction of the final query

takes about 0.5 seconds per query. Retrieval times vary tremendously with

length of query. We ran in batch mode, constructing an inverted file for the entire D3

document set ("Inc" document weights) and

then comparing a query against that inverted,

file. Not only is this not what would be done

in practice, but it is much less efficient than

would be done in practice given our massive

expansion of queries: for each query in crnlRl,

well over half the entire inverted file was read! CPU time per query ranged from about 5 sec- onds (no expansion) to 65 seconds (expansion

by 500 terms).

Conclusion

No firm conclusions can be reached regarding

the usefulness of combining local and global

similarities in the TREC environment. In some limited circumstances minor improvements can be obtained, but in general we have

not (yet!) been able to take advantage of the

local information we know should be useful.

52
OllPTV P}ir3.sc Single Phrase Weifflit Weight

Import Exp3-ncl Expand. R flp VJl Tl t 11N nT*-rplp 11 J, tievaV CLlln fu 51 0.0 500 8 94

0.0 200 ao 1 fi n

1 0 iiOO eo TrO A n n 900 Qo

A

*x 0.0 200 fio 94. So 56 1.0 0 Q g g 4 57 0.0 500 QV 94 4 58 1.0 500 0 o 48^o 4

0.5 100 1 n fio

ao 4

60 1.0 0

fio»o 4

61 0.0 0 g g 4

62 0.0 500 g 94 4 63 1.0 0 Q

Qo

ao 4 64 0.0 500 Qo 1 fi 4

1 0 900 0 fio 1 fi 4 66 1.0 200 nU

0o 1 fi 4

67 0.0 500 fio 1 fi 4 68 1.0 300 0 g 48 4

69 1.0 500 0 8 24 4

70 1.0 500 50 8 48 4

71 0.0 500 8 8 4 72 1.0 300 0 g 48 4

73 0.0 500 8 16 4 74 1.0 100 100 8 8 4

75 0.0 100 8 24 6 76 1.0 300 0 8 24 4 11 0.0 500 8 8 4

78 0.0 0 8 24 4

79 0.0 500 g 24 4

80 1.0 500 0 8 24 4

81 1.0 500 0 8 32 4

82 1.0 500 0 8 32 4

83 0.0 500 8 94 4 84 0.0 100 8 36 4

85 1.0 100 10 8 24 4

86 0.0 30 8 24 4

87 1.0 500 0 g 48 4

88oo 1 0 'iOO 50 8 48 4 89 1.0 500 0 8 48 4 90 0.0 500 8 16 4

91 1.0 0 0 8 24 4

92 0.0 100 8 36 4

93 1.0 100 50 8 8 4

94 0.0 200 8 36 4

95 0.0 200 8 16 8 96 1.0 300 50 8 48 4

97 0.0 200 8 36 4

98 0.0 500 8 24 4 99 0.0 50 8 24 4 100 1.0 500 50 8 48 4

Table 5: Optimum routing parameters, query-by-query

53
X.Y A.B.C Total RpI

1. no fdbk 0.0 8.0.0 3382 6509 2869

2. no expand 0.0 8.8.4 3531 6849 3087

3. little expand 20.10 8.8.4 3756 7192 3345

4. crnlRl 300.50 8.16.4 4273 7764 3952

5. crnlCl varies varies 4367 7808 4091

Table 6: Routing evaluation

For TREC 2, this failure is ameliorated by the

base level performance of the global run. If the correct weights are used, the effectiveness of automatic indexing is extremely good.

Automatic massive query expansion proves

to be very effective for routing. Conventional relevance feedback techniques are used

to weight the expanded queries. Parameters

for the relevance feedback algorithms are esti- mated both over all the queries and for each

query individually. The individual query esti- mation perform better (3-4%) but by an in- sufficient amount to be convincing.

References

[1] Chris Buckley, Gerard Salton, and James

Allan. Automatic retrieval with locality

information using SMART. In D. K. Bar- man, editor. Proceedings of the First Text REtrieval Conference (TREC-1), pages

59-72. NIST Special Publication 500-207,

March 1993.

[2] James P. Callan and W. Bruce Croft. An evaluation of query processing strate- gies using the tipster collection. In Proceedings of the Sixteenth Annual Inter- national ACM SIGIR Conference on Research and Development in Information

Retrieval, pages 347-355, June 1993.

[3] Norbert Fuhr. Models for retrieval with

probabilistic indexing. Information Processing and Management, 25(l):55-72,

1989.

[4] Norbert Fuhr and Chris Buckley. Automatic structuring of text files. ACM

Transactions on Information Systems,

9(3):223-248, 1991.

[5] Norbert Fuhr and Chris Buckley. Optimizing document indexing and search

term weighting based on probabilistic

models. In D. K. Harman, editor. Proceedings of the First Text REtrieval Conference (TREC- 1), pages 89-99. NIST

Special Publication 500-207, March 1993.

[6] D. Harman. Towards interactive query

expansion. In Proceedings of the Eleventh

International Conference on Research

and Development in Information Retrieval, pages 321-331. Association for Computing Machinery, 1988.

[7] Marti A. Hearst and Christian Plaunt.

Subtopic structuring for full-length document access. In Proceedings of the Six- teenth Annual International ACM SIGIR

Conference on Research and Development

in Information Retrieval, pages 59-68,

June 1993.

[8] E. Ide. New experiments in rele- vance feedback. In Gerard Salton, editor. The SMART Retrieval System —

Experiments in Automatic Document

Processing, chapter 16. Prentice Hall, Englewood Cliffs, NJ, 1971.

[9] J.J. Rocchio. Relevance feedback in in- formation retrieval. In Gerard Salton,

editor, The SMART Retrieval System—

Experiments in Automatic Document

Processing. Prentice Hall, Englewood

Cliffs, NJ, 1971.

[10] Gerard Salton. Automatic Text Processing — the Transformation, Analysis and

Retrieval of Information by Computer.

Addison-Wesley Publishing Co., Reading,

MA, 1989.

[11] Gerard Salton. Developments in auto- matic text retrieval. Science, 253:974-

980, August 1991.

54
[12] Gerard Salton and Chris Buckley. Termweighting approaches in automatic text

retrieval. Information Processing and

Management, 24(5):513-523, 1988.

[13] Gerard Salton and Chris Buckley. Improving retrieval performance by rele- vance feedback. Journal of the American Society for Information Science,

41(4):288-297, 1990.

[14] Gerard Salton and Chris Buckley. Automatic text structuring and retrieval: Experiments in automatic encyclopedia

searching. In Proceedings of the Fourteenth Annual International ACM SIGIR

Conference on Research and Development

in Information Retrieval, pages 21-30,

1991.

[15] Gerard Salton and Chris Buckley. Global

text matching for information retrieval. Science, 253:1012-1015, August 1991.

[16] Gerard Salton, Chris Buckley, and James

Allan. Automatic structuring of text files. Electronic Publishing, 5(1):1-17, March

1992.

[17] Craig Stanfill and David L. Waltz. Statistical methods, artificial intelligence, and

information retrieval. In Paul S. Jacobs, editor, Text-based Intelligent Sys- tems: Current Research and Practice

in Information Extraction and Retrieval.

Lawrence Erlbaum Associates, Inc., Hills- dale, NJ, 1971.

55

Full Text Retrieval based on Probabilistic Equations

with Coefficients fitted by Logistic Regression

Wm. S. Cooper

Aitao Chen

Fredric C. Gey

S.L.I.S., University of California

Berkeley, CA 94720

ABSTRACT

The experiments described here are part of a research

program whose objective is to develop a full-text retrieval methodology that is statistically sound and

powerful, yet reasonably simple. The methodology is based on the use of a probabilistic model whose

parameters are fitted empirically to a learning set of

relevance judgements by logistic regression. The

method was applied to the TIPSTER data with optimally relativized frequencies of occurrence of match

stems as the regression variables. In a routing

retrieval experiment, these were supplemented by

other variables corresponding to sums of logodds asso- ciated with particular match stems.

Introduction

The full-text retrieval design problem is largely a problem in the combination of statistical evidence. With

this as its premise, the Berkeley group has concentrated on

the challenge of finding a statistical methodology for combining retrieval clues in as powerful a way as possible, consistent with reasonable analytic and computational

simplicity. Thus our research focus has been on the general logic of how to combine clues, with no attempt made

at this stage to exploit as many clues as possible. We feel

that if a straightforward statistical methodology can be

found that extracts a maximum of retrieval power from a few good clues, and the methodology is clearly hospitable

to the introduction of further clues in future, progress will have been made.

We join Fuhr and Buckley (1991, 1992) in thinking

that an especially promising path to such a methodology is

to combine a probabilistic retrieval model with the tech- niques of statistical regression. Under this approach a

probabilistic model is used to deduce the general form that

the document-ranking equation should take, after which

regression analysis is applied to obtain empirically-based

values for the constants that appear in the equation. In

this way the probabilistic theory is made to constrain the universe of logically possible reuieval rules that could be

chosen, and the regression techniques complete the choice

by optimizing the model's fit to the learning data. The probabilistic model adopted by the Berkeley

group is derived from a statistical assumption of 'linked dependence'. This assumption is weaker than the historic independence assumptions usually discussed. In its simplest form the Berkeley model also differs fi"om most traditional models in that it is of 'Type 0' ~ meaning that the analysis is carried out w.r.t. sets of query-document pairs rather than w.r.t. particular queries or particular documents. (For a fuller explanation of this typology see Robertson, Maron & Cooper 1982.) But when relevance

judgement data specialized to the currently submitted

search query is available, say in the form of relevance

feedback or routing history data, the model is flexible enough to accommodate it (resulting in 'Type 2' retrieval.)

Logistic regression (see e.g. Hosmer & Lemeshow

(1989)) is the most appropriate type of regression for this kind of IR prediction. Although standard multiple regres- sion analysis has been used successfully by others in comparable circumstances (Fuhr & Buckley op. cit), we

believe logistic regression to be logicaUy more appropriate for reasons set forth elsewhere (Cooper, Dabney &

Gey 1992). Logistic regression, which accepts binary

training data and yields probability estimates in the form

of logodds values, goes hand-in-glove with a probabilistic IR model that is to be fitted to binary relevance judgement

data and whose predictor variables are themselves

logodds.

57
The Fundamental Equation

As the starting point of our probabilistic retrieval model we adopt (with reservations to be explained

presently) a statistical simplifying assumption called the 'Assumption of Linked Dependence' (Cooper 1991).

Inmitively, it states that in the universe of all query- document pairs, the degree of statistical dependency that

exists between properties of pairs in the subset of all rele- vance-related pairs is linked in a certain way with the degree of dependency that exists between the same pairproperties in the subset of all nonrelevance-related pairs.

Under at least one crude measure of degree of dependence

(specifically, the ratio of the joint probability to the product of individual probabilities), the Unkage in question is one of identity. That is, the claim made by the assumption

is that the degree of the dependency is the same in both

sets.

For pair-properties Ai Ai^,the mathematical

statement of the assumption is as follows. ASSUMPTION OF LINKED DEPENDENCE:

P(Ai,...,An\ R) _ PjAj I R) PjA^ I R)

P(Ai,...,An \ R) ~ PiAi\R)^ "' ^ PiAs\R)

If one thinks of a query and document drawn at random, R

is the event that the document is relevant to the query, while each clue i4, is some respect in which the document

is similar or dissimilar to, or somehow related to, the query.

For most clue-types likely to be of interest this simplifying assumption is at best only approximately true. Though not as implausible as the strong conditional inde- pendence assumptions traditionally discussed in probabilistic IR, still it should be regarded with skepticism. In

practice, when the number N of clues to be combined

becomes large the assumption can become highly unrealistic, widi a distorting tendency that often causes the probability of relevance estimates produced by the resulting

retrieval model to be grossly overstated for documents

near the top of the output ranking. But it will be expedient here to adopt the assumption anyway, on the understanding that later on we shall have to find a way to curb

its probability-inflating tendencies.

From the Assumption of Linked Dependence it is possible to derive the basic equation underlying the probabilistic model:

Equation (1):

logO(/?IAi ,...,An) =

log 0(R) + i [ log 0(R I Ai) - log 0(R) ] 1=1

where for any events E and E' the odds 0(E / E') is by

P(E I E')

definition

j

Using this equation, the evidence of

separate clues can be combined as shown in the right side to yield the logodds of relevance based on all clues,

shown on the left. Query-document pairs can be ranked

by this logodds estimate, and a ranking of documents for a

particular query by logodds is of course a probability

ranking in the IR sense. For further discussion of the linked dependence assumption and a formal derivation of

Eq. (1) from it, see Cooper (1991) and Cooper, Dabney &

Gey (1992). An empirical investigation of independence

and dependence assumptions in IR is reported by Gey

(1993).

Application to Term Properties

We shall consider as a first appUcation of the modeling equation the problem of how to exploit the properties

of match terms. By a 'match' term we mean a stem (or

word, phrase, etc.) that occurs in the query and also, in

identical or related form, in the document to which the query is being compared. The retrieval properties of a match term could include its frequency characteristics in the query, in the document, or in the collection as a whole,

its grammatical characteristics, the type or degree of

'match' involved, etc. If there are M match terms that

relate a certain query to a certain document, Eq. (1) becomes applicable with set to M. Each of the properties Ax,..., Am then represents a composite clue or set of

properties concerning one of the query-document pair's

match terms.

Suppose a 'learning set' of human judgements of

relevance or nonrelevance is available for a sample of rep- resentative query-document pairs. (However, for the time

being we assume no such learning data is available for the very queries on which the retrieval is to be performed.)

Logistic regression can be applied to the data to develop

an equation capable of using the match term clues to esti- mate, for any query-document pair, values for each of the expressions in the right side of Eq. (1). Eq. (1) then yields a probability estimate for the pair.

58
TABLE I: Equations to Approximate the Components of Eq. (1)

logO(/?)= [bo + biM]

logO(^Mi)-log(9(/?) « [ao + aiXi,i + .-- + fljfXi,^ + aj^+iM] - [bo + biM]

\ogO(R\AM)-logO(R) [ao + aiXM,i + --- + aKXM,K + aK+iM] - [bo + b^M]

M M

logO(/?Ui ,...,Am) «[aoM + fli I + 2 X^,K + aK^:M^]-{M-l)[bo + b:M]

What must be done, then, is to find approximating

expressions for the various components in the right side of

Eq. (1). Table I (above) shows the interrelationships

among the quantities involved. In this array, the material

to the left of the column of approximation signs just

restates Eq. (1), for Eq. (1) asserts that the expressions

above the horizontal line sum to the expression below the

line. On the right of each approximation sign is a simple

formula that might be used to approximate the expression on the left. (More complex formulae could of course also be considered.) Each right-side formula involves a linear combination of K variables X^ i

, . . . , X^jc corresponding

to the K retrieval properties (frequency values, etc.) used

to characterize the term in question. The idea is to apply

logistic regression to find the values for the coefficients

(the a's and fe's) in the right side that produce the best regression fit to the learning sample. Once these constants have been determined, retrieval can be carried out by sum- ming the right sides to obtain the desired estimate of

log 0(R\ Ai A{^) for any given query-document

pair.

It may not be immediately apparent why terms in M

have been included in the approximating formulas on the

right. In Eq. (1), the number of available retrieval clues

is actually part of the conditionalizing information, a fact

that could have been stressed by writing 0(7? I A^) in place

of OiR) and 0(R I ^„ N) in place of 0{R I Ai). So the approximating formula for log OiR) is really an approximation for log 0(R\ M) and must involve a term in M.

(Simple functions of M such as Vm or log (M) might also be considered.) Similar remarks apply to the formulas for

approximating log (R I A„).

There are two approaches to the problem of finding

coefficients to use in the approximating expressions. The

first is what might be called the 'triples-then-pairs'

approach. It starts out with a logistic regression analysis

performed directly on a sample of query-document-term

triples constructed from the learning set. In the sample,

each triple is accompanied by the clue-values associated

with the match term in the query and document, and by

the human relevance judgement for the query and document. The clue-values are used as the values of the independent variables in the regression, and the relevance

judgements as the values of the the dependent variable. After the desired coefficients have been obtained, the

resulting regression equation can be appUed to evaluate

the left-side expressions, which can in turn be summed

down the M terms to obtain a preliminary estimate of

log OiR \A^,..., Am).

A second regression analysis is then performed on a sample of query-document pairs, using the preUminary

logodds prediction from the first (triples) analysis as an

independent variable. This second analysis serves the pur- pose of correcting for distortions introduced by the Assumption of Linked Dependence. It also allows

retrieval properties not associated with particular match

terms, such as document length, age, etc., to be intro- duced. The triples-then-pairs approach is the one used by

the Berkeley group in its Trec-1 research (Cooper, Gey &

Chen 1992). The theory behind it is presented in more

detail in (Cooper, Dabney & Gey 1992).

59
The second way of determining the coefficients ~

the one used in Trec-2 - is the 'pairs-only' approach. It

requires only one regression analysis, performed on a sample of query-document pairs. It is based on the trivial observation that in the right side of the above array, instead of adding across rows and then down the resulting

column of sums, one can equivalently add down columns

and across the resulting row of sums. Under either proce- dure the grand total value for log 0{R \ Ax,...,Am) will be the same. Summing down the columns and then across the

totals gives the expression shown in the bottom line of the

array. It simplifies to

logO(/?Ui ,...,Ati) « M M

m=l m=l + (ao -bo + bi)M + (qk+i - bi)M^

Since there is no need to keep the Oj coefficients segre- gated from the bj coefficients to get a predictive equation,

this suggests the adoption of a regression equation of form

log 0(R\Ai,..., Am) = M M

Co + Ci X ^m,\ + • • • + Ca: X ^m,K

m=l m=l

The coefficients Cq,..., Ck+2 may be found by a logistic

regression on a sample of query-document pairs constructed from the learning sample. In the sample each pair

is accompanied by its K different X^^t -values each

already summed over all match terms for the pair, the val- ues of M and M^, and (to serve as the dependent variable

in the regression) the human relevance judgement for the

pair.

But if only one level of regression analysis is to be

performed, where is the correction for the Assumption of

Linked Dependence to take place? That assumption

causes mischief because it creates a tendency for the pre- dicted logodds of relevance to increase roughly Unearly

with the number of match terms, whereas the true increase

is less than hnear. This can be corrected by modifying the variables in such a way that their values rise less rapidly than the number of match terms as the number of match

terms increases. The variables can, for instance, be multi- plied by some function /(M) that drops gently with

increasing M, say or -

^——. The exact form of

Vm 1 + logM

the function can be decided during the course of the regression analysis.

With such a damping factor included, the foregoing

regression equation becomes

Equation (2):

log - M M

C 0+ Ci f{M) X +---+CK /(M) X X„,K

1 m=l

In our experiments, this simple modification was found to improve the regression fit and the precision/recall perfor- mance. It would appear therefore to be a worth-while

refinement of the basic model. Note, however, that this adjustment only removes a general bias. It does nothing

to exploit the possibility of measuring dependencies

between particular stems to improve retrieval effective- ness. To exploit individual dependencies would be desir- able in principle, but would require a substantial elaboration of the model for what might well turn out to be an

insignificant improvement in effectiveness (for discussion see Cooper (1991)).

OptimaUy Relativized Frequencies

The philosophy of the project called for the use of a few well-chosen retrieval clues. The most obvious clues

to be exploited in connection with match terms are their frequencies of occurrence in the query and document.

What is not so obvious is the exact form the frequencies

should take. For instance, should they be absolute or relative frequencies, or something in between?

The IR literature mentions two ways in which fre- quencies might be relativized for use in retrieval. The first

is to divide the absolute frequency of occurrence of the term in the query or document by the length of the query

or document, or some parameter closely associated with

length. The second is to divide the relative frequency so obtained by the relative frequency of occurrence of the term in the entire collection considered as one long run- ning text. Both kinds of relativization seem potentially

beneficial, but the question remains whether these relativizations, if they are indeed helpful, should be carried out in full strength, or whether some sort of blend of absolute and relative frequencies might serve better. To answer this question, regression techniques were

used in a side investigation to discover the optimal extent

60
to which each of the two kinds of relativization might best be introduced. To investigate the first kind of relativization ~ relativization with respect to query or document

length - a semi-relativized stem firequency variable of the form

absolute frequency

document length + C

was adopted. If the constant C is chosen to be zero, one

has full relativization, whereas a value for C much greater

than the document length will cause the variable to behave

in the regression analysis as though it were an absolute frequency. Several logistic regressions were run to dis- cover by trial-and-error the value of C that produced the

best regression fit to the TIPSTER learning data and the highest precision and recall. It was found that a value of

around C = 35 for queries, and C = 80 for documents,

optimized performance.

For the relativization with respect to the global relative frequency in the collection, the relativizing effect of

the denominator was weakened by a slightly different method - by raising it to a power less than 1.0. For the document frequencies, for instance, a variable of form

absolute frequency in doc

document length + 80

[ relative frequency in collection ] ^

with D < 1 was in effect used. Actually, it was the logarithm of this expression that was ultimately adopted,

which allowed the variable to be broken up into a differ- ence of two logarithmic expressions. The optimal value of the power D was therefore obtainable in a single

regression as the coefficient of the logged denominator.

Thus the variables ultimately adopted consisted in essence of sums over two optimally relativized frequencies CORF's) " one for match stem frequency in the query and one for match stem frequency in the document.

Because of the logging this breaks up mathematically into three variables. A final logistic regression using sums

over these variables as indicated in Eq. (2) produced the ranking equation

Equation (3):

log 0(R\Ai,..., Am) «

-3.51 + -pJ— O + 0.0929 A/

where O is the expression

M M M

37.4 2 +0.330 X ^..2 -0. 1937 L X„^,

m=l m=l m=l

Here X„ 1 = number of times the w'th stem occurs in the

query, divided by (total number of all stem occur- rences in query + 35);

X„2 - number of times the /n'th stem occurs in the document, divided by (total number of all stem

occurrences in document + 80), quotient logged;

X^ -i = number of times the /n'th stem occurs in the

collection, divided by the total number of all stem

occurrences in the collection, quotient logged;

M = number of distinct stems common to both query and document.

Although Eq. (2) calls for an term as well, such a term was found not to make a statistically significant contribution and so was eliminated.

Eq. (3) provided the ranking rule used in the ad hoc

run (labeled 'Brkly3') and the first routing run ('Brkly4')

submitted for Trec-2. The equation is notable for the sparsity of the information it uses. Essentially it exploits only

two ORE values for each match stem, one for the stem's frequency in the query and the other for its frequency in the document. Other variables were tried including the inverse document frequency (both logged and unlogged), a variable consisting of a count of all two-stem phrase

matches, and several variables for measuring the tendency

of the match stems to bunch together in the document. All of these exhibited predictive power when used in isolation, but were discarded because in the presence of the ore's none produced any detectable improvement in the regression fit or the precision/recall performance. Some

attempts at query expansion using the WordNet thesaurus

also failed to produce noticable improvement, even when

care was taken to create separate variables with separate

coefficients for the synonym-match counts as opposed to the exact-match counts. The quality of a retrieval output ranking matters most near the top of the ranking where it is likely to affect the most users, and matters hardly at all far down the ranking where hardly any users are apt to search. Because

of this it is desirable to adopt a sampUng methodology that produces an especially good regression fit to the sample

61
data for documents with relatively high predicted probabilities of relevance. In an attempt to achieve this emphasis, the regression analysis was carried out in two steps. A

logistic regression was first performed on a stratified sample of about one-third of the then-available TIPSTER rele- vance judgements to obtain a preUminary ranking rule. This preliminary equation was then applied as a screening

rule to the entire available body of judged pairs, and for each query all but the highest-ranked 500 documents were

discarded. The resulting set of 50,000 judged query- document pairs (100 topics x 500 top-ranked docs per

topic) served as the learning sample data for the final regression equation displayed above as Eq. (3).

Application to Query-Specific Learning Data

To this point it has been assumed that relevance

judgement data is available for a sample of queries typical

of the future queries for which retrieval is to be per- formed, but not for those very queries themselves. We

consider next the contrasting situation in which the learn- ing data that is available is a set of past relevance judge- ments for the very queries for which new documents are

to be retrieved. Such data is often available for a routing or SDI request for which relevance feedback has been col- lected in the past, or for a retrospective feedback search

that has akeady been under way long enough for some

feedback to have been obtained.

For a query so equipped with its very own learning

sample, it is possible to gather separate data about each

individual term in the query. Such data reflects the term's

special retrieval characteristics in the context of that query. For instance, for a query term T, we may count the number n{Ti, R) of documents in the sample that contain the term and are also relevant to the query, the number of documents n(Ti,R) that contain the term but are not

relevant to the query, and so forth. The odds that a future document will turn out to be

be relevant to the query, given that it contains the term, can be estimated crudely as

0{R\ Ti) =

njTj, R) + /J n(R)

n{Ti,R) + p n{R)

The smaller the value assigned to yff, the more influ- ence the sample evidence will have in moving the esti- mate away from the prior odds. The adjustment causes

large samples to have a greater effect than small, as seems

natural. It also forestalls certain absurdities implicit in the unadjusted formula, for instance the possibility of calculating infinite odds estimates.

Suppose now that a query contains Q distinct terms Ti,...,Tm,Tm+i,...,Tq, numbered in such a way that

the first M of them are match terms that occur in the document against which the query is to be compared. The fun- damental equation can be applied by taking N to be Q,

and interpreting the retrieval clues Ai, . . , , A^r as the pres- ence or absence of particular query terms in the document.

The pertinent data can be organized as shown in Table II (next page).

Thus we are led to postulate, for query-specific

learning data, a retrieval equation of form

Equation (4):

log WlAi Aq) -

Co + Ci/(0

where

OiR I r,) - n(Ti,RJ

n(Ti,R)

^ , n{Ti,R) + pn{R)

Oi = > I02 = =- tx ^ n{Ti,R) + pn{R)

i ,og«±«S

m=M+i ^ n{Ti,R)^pn{R)

03= (e-l)log^

and where f{Q) is some restraining function intended as before to subdue the inflationary effect of the Assumption

of Linked Dependence. The coefficients Cq, Cx are found

by a logistic regression over a sample of query-document

pairs involving many queries.

To refine this estimate, a Bayesian trick adapted from

Good (1965) is useful. One simply adds the two figures

used in expressing the prior odds of relevance (i.e. n (R)

and n (R)) into the numerator and denominator respectively with an arbitrary weight /} as follows:

Combining Query-Specific and Query-Nonspecific

Data

If a query-specific set of relevance judgements is available for the query to be processed, a larger

62
TABLE II: Reinterpretation of the Components of Eq. (1) for Routing Retrieval

log 0(R) =

, n(R)

log 0(R\ AO

log OiR) = n(T„R) + fi n{R)

^ niT,,R)^Pn{R)

, n(R)

'''niR)

•

log 0(R\ Am) - log 0(R) =

n(i M,R) + P n{K)

n{jM.R) + Pn{R)

log"^-^ ^n{R)

log 0(R\Am^i)-- log OiR) =

niTM^^,R) + /3n(R)

n{TM^y,R) + Pn{,R)

, n{R)

'''niR)

log 0(R\Aq)- log 0{R) = J n{TQ,R) + pn{R)

n(TQ,R) + lin{R) '""'niR)

log 0(R\Ai ,. ..,Aq) = M n{T„R) + /in{R)

2^ log = =^ + r ^n(Ti,R) + fin(R) tx ^n{Ti,R) + finiR) '''''' MR)

nonspecific set may well be available at the same time. If so the theory developed in the foregoing section can be

^plied in conjunction with the earlier theory to capture

the benefits of both kinds of learning sets. The retrieval equation will then contain variables not only of the kind

occurring in Eq. (4) but also of the Eq. (2) kind.

It is convenient to formulate this equation in such a way that it contains as one of its terms the entire ranking

expression developed earlier for the nonspecific learning

data. For the TIPSTER data the combined equation takes

the form:

Equation (5): logO(R\A„...,AM>Al---^AQ)^

0. 688 4)4 + 0. 344 + O2 - j + 0. 0623

where is the entire right side of Eq. (3) and , O2, 03

are as defined in Eq. (4). This form for the equation is computationally convenient if Eq. (3) is to be used as a

preliminary screening rule to eliminate unpromising

documents, with Eq. (5) in its entirety applied only to rank

those that survive the screening.

Eq. (5) was used to produce the Trec-2 routing run

'Brkly5'. Its coefficients were determined by a logistic

regression constrained in such a way as to make the query-specific variables contribute about twice as heavily

as the nonspecific, when contribution is measured by stan- dardized coefficient size. This emphasis was largely arbitrary; finding the optimal balance between the queryspecific and the general contributions remains a topic for

future research. A value of 20 /«(/?) was used for y9. This

choice too was arbitrary, and it would be interesting to try

to optimize it experimentally for some typical collection

(trying out, perhaps, numbers larger than 20, divided by

the total number of documents in the query's learning set). No restraining function f{Q) was used in the final form of

Eq. (5) because none that were tried out produced any dis- cernible improvement in fit or retrieval effectiveness in

this context.

63
Calibration of Probability Estimates

The most important role of the relevance probability

estimates calculated by a probabilistic IR system is to rank

the output documents in as effective a search order as possible. For this ranking function it is only the relative sizes of the probability estimates that matter, not their absolute magnitudes. However, it is also desirable that the absolute

sizes of these estimates be at least somewhat realistic. If they are, they can be displayed to provide guidance to the

users in their decisions as to when to stop searching down

the ranking. This capability is a potentially important side

benefit of the probabilistic approach.

One way of testing the realism of the probability

estimates is to see whether they are 'well-calibrated'. Good calibration means that when a large number of probability estimates whose magnitudes happen to fall in a certain small range are examined, the proportion of the trials

in question with positive outcomes also falls in or close to

that range. To test the calibration of the probability predictions produced by the Berkeley approach, the 50,000

query-document pairs in the ad hoc entry Brkly3 along

with their accompanying relevance probability estimates were sorted in descending order of magnitude of estimate. Pairs for which human judgements of relevancerelatedness were unavailable were discarded; this left 22,352 sorted pairs for which both the system's probability estimates of relevance and the 'correct' binary judge- ments of relevance were available. This shorter list was

divided into blocks of 1,000 pairs each ~ the tiiousand

pairs with the highest probability estimates, the thousand

with the next highest, and so forth. Within each block the

'actual' probability was estimated as the proportion of the 1,000 pairs that had been judged to be relevance-related by humans. This was compared against the mean of all the system probability estimates in the block. For a well- calibrated system these figures should be approximately

equal.

The results of the comparison are displayed in Table

III. It can be seen that the system's probability predictions, while not wildly inaccurate, are generally somewhat

higher than the actual proportions of relevant pairs. The

same phenomenon of mild overestimation was observed

when the runs Brkly4 and Brkly5 were tested for well- calibratedness in a similar way.

Since no systematic overestimation was observed

when the calibration of the formula was originally tested against the learning data, it seems likely that the

TABLE ni: Calibration of Ad Hoc

Relevance-Probability Estimates

Query-doc Mean System Proportion

Pair Probability Actually

Ranks Estimate Relevant

Ito 1,000 0.66 0.60

1,001 to 2,000 0.63 0.47

2,001 to 3,000 0.61 0.44

3,001 to 4,000 0.58 0.41

4,001 to 5,000 0.55 0.38

5,001 to 6,000 0.53 0.34

6,001 to 7,000 0.50 0.36

7,001 to 8,000 0.48 0.36

8,001 to 9,000 0.46 0.36

9,001 to 10,000 0.44 0.38

10,001 to 11,000 0.42 0.39

11,001 to 12,000 0.41 0.36

12,001 to 13,000 0.39 0.37

13,001 to 14,000 0.37 0.36

14,001 to 15,000 0.36 0.35

15,001 to 16,000 0.34 0.31 16,001 to 17,000 0.32 0.29

17,001 to 18,000 0.31 0.28

18,001 to 19,000 0.29 0.23

19,001 to 20,000 0.28 0.22

20,001 to 21,000 0.25 0.21 21,001 to 22,000 0.23 0.23

22,001 to 22,352 0.18 0.19

overestimation seen in the table is due mainly to the shift from learning data to test data. Naturally, predictive for- mulae that have been fine-tuned to a certain set of learning data will perform less well when applied to a new set of

data to which they have not been fine-tuned. If this is indeed the root cause of the observed overestimation, it could perhaps be compensated for (at least to an extent

sufficient for practical purposes) by the crude expedient of

lowering all predicted probabilities to, say, around four

fifths of their originally calculated values before displaying them to the users.

Computational Experience

The statistical program packages used in the course of the analysis included SAS, S, and BLSS. Of these,

64
SAS provides the most complete built-in diagnostics for

logistic regression. BLSS was found to be especially convenient for interactive use in a UNIX environment, and

ended up being the most heavily used. The prototype retrieval system itself was implemented as a modification of the SMART system with

SMART'S vector-similarity subroutines replaced by the

probabilistic computations of Eqs. (3) and (5). For the runs BrklyS and Brkly4, which used only Eq. (3), only

minimal modifications of SMART were needed, and at run time the retrieval efficiency remained essentially the same as for the unmodified SMART system. This demonstrates that probabilistic retrieval need be no more cumbersome computationally than the vector processing alternatives. For Brkly5, which used Eq. (5), the modifications were somewhat more extensive and retrieval took about

20% longer.

Retrieval Effectiveness

The Berkeley system achieved an average precision over all documents (an '11-point average') of 32.7% for

the ad hoc retrieval run Brkly3, and 29.0% and 35.4%

respectively for the routing runs Brkly4 and BrklyS. The

distinct improvement in effectiveness of BrklyS over Brkly4 suggests that in routing retrieval the use of fre- quency information about individual query stems is worth

while.

At the '0 recall level' a precision of 84.7%, the highest recorded at the conference, was achieved in the ad

hoc run. The high effectiveness of the Berkeley system

for the first few retrieved documents may be explainable

in terms of the practice, mentioned earlier, of redoing the regression analysis for the highest-ranked 500 documents

for each query. This technique ensures an especially good

regression fit for the query-document pairs that are especially likely to be relevant, thus emphasizing good perfor- mance near the top of the ranking where it is most important. The generally high retrieval effectiveness of the Berkeley system should be interpreted in the light of the

fact that the system probably uses less evidence - that is, fewer retrieval clues — than any of the other highperforming TREC-2 systems. In fact, the only clues used were the frequency characteristics of single stems (not even phrases were included). What this suggests is that the underlying probabilistic logic may have the capacity to exploit exceptionally fully whatever clues may be

available.

Summary and Conclusions

The Berkeley design approach is based on a probabilistic model derived from the linked dependence

assumption. The variables of the probability-ranking

retrieval equation and their coefficients are determined by

logistic regression on a judgement sample. Though the model is hospitable to the utilization of other kinds of evi- dence, in this particular investigation the only variables used were optimally relativized frequencies (ORF's) of

match stems. The approach was found to have the following

advantages:

1. Experimental Efficiency. Since the numeric coefficients in a regression equation are determined

simultaneously in one computation, trial-and-error experimentation involving the evaluation of

retrieval output to optimize parameters is largely

avoidable.

2. Computational Simplicity. For ad hoc retrieval and

routing retrieval that does not involve individual stem statistics, the computational simplicity and

efficiency achieved by the model at run time are comparable to that of simple vector processing

retrieval models. For routing retrieval that exploits individual stem frequencies the programming is somewhat more complicated and runs slightly slower.

3. Effective Retrieval. The level of retrieval effective- ness as measured by precision and recall is high relative to the simple clue-types used.

4. Potential for Well-Calibrated Probability Estimates.

In-the-ballpark estimates of document relevance

probabilities suitable for output display would

appear to be within reach.

Acknowledgements

We are indebted to Ray Larson and Chris Plaunt for helpful systems advice, as well as to the several col- leagues already acknowledged in our Trec-1 report. The

work stations used for the experiment were supplied by

the Sequoia 2000 project at the University of California, a

project principally funded by the Digital Equipment Corporation. A DARPA grant supported the progranmiing

effort.

65
References

Cooper, W. S. (1991). Inconsistencies and Mis- nomers in Probabilistic IR. Proceedings Fourteenth

Annual International ACM SIGIR Conference on

Research and Development in Information Retrieval. Chicago, 57-62.

Cooper, W. S.; Dabney, D.; Gey, F. (1992). Probabilistic retrieval based on staged logistic regression. Proceedings of the Fifteenth Annual International ACM

SIGIR Conference on Research and Development in Infor- mation Retrieval. Copenhagen, 198-210.

Cooper, W. S.; Gey, F. C; Chen, A. (1993). Probabilistic retrieval in the TIPSTER Collections: An Application of Staged Logistic Regression. The First Text REtrieval Conference (TREC-1). NIST Special Publication 500-207. Washington, 73-88.

Fuhr, N.; Buckley, C. (1993). Optimizing Document Indexing and Search Term Weighting Based on

Probabilistic Models. The First Text REtrieval Confer- ence (TREC-1). NIST Special Publication 500-207.

Washington, 89-100.

Fuhr, N.; Buckley, C. (1991). A probabilistic learn- ing approach for document indexing. ACM Transactions on Information Systems, 9, 223-248.

Gey, F. C. (1993). Probabilistic Dependence and

Logistic Inference in Information Retrieval Ph.D. Dissertation, Univ. of California, Berkeley, California. Good, I. J. (1965). The estimation of probabilities;

An essay on modern Bayesian methods. Cambridge,

Mass.; M.I.T. Press.

Hosmer, D. W; Lemeshow, S. (1989). Applied

Logistic Regression. New York: Wiley.

Robertson, S. E.; Maron, M. E.; Cooper, W. S. (1982). Probability of relevance: A unification of two

competing models for document retrieval. Information

Technology: Research and Development 1,1-21.

66
Probabilistic Learning Approaches for Indexing and Retrieval with the

TREC-2 Collection

Norbert Fuhr, Ulrich Pfeifer, Christoph Bremkamp, Michael Pollmann

University of Dortmund, Germany

Chris Buckley

Cornell University

Abstract

In this paper, we describe the application of probabilistic models for indexing and retrieval with the TREC-2

collection. This database consists of about a million

documents (2 gigabytes of data) and 100 queries (50

routing and 50 adhoc topics). For document indexing, we use a description-oriented approach which exploits

relevance feedback data in order to produce a probabilistic indexing with single terms as well as with phrases.

With the adhoc queries, we present a new query term

weighting method based on a training sample of other

queries. For the routing queries, the RPI model is applied which combines probabilistic indexing with query

term weighting based on query-specific feedback data. The experimental results of our approach show very good performance for both types of queries.

1 Introduction

The good TREC-1 results of our group described in [Fuhr Sz Buckley 93] have confirmed the general concept

of probabilistic retrieval as a learning approach. In this paper, we describe some improvements of the indexing

and retrieval procedures. For that, we first give a brief

outline of the document indexing procedure which is based on description-oriented indexing in combination

with polynomial regression. Section 3 describes query

term weighting for adhoc queries, where we have devel- oped a new learning method based on a training sample of other queries and corresponding relevance judge- ments. In section 4, the construction of the routing

queries is presented, which is based on the probabilistic

RPI retrieval model for query-specific feedback data. In the final conclusions, we suggest some further improvements of our method.

2 Document indexing

The task of probabilistic document indexing can be de- scribed as follows (see [Fuhr & Buckley 91] for more details): Let dm denote a document, ti a term and R the

fact that a query-document pair is judged relevant, then

P{R\ti,dm) denotes the probability that document dm

will be judged relevant w.r.t. an arbitrary query that

contains term ti. Since these weights can hardly be es- timated directly, we use the description-oriented indexing approach. Here term-document pairs {ti,dm) are mapped onto so-called relevance descriptions x(ti,dm)-

The elements Xi of the relevance description contain val- ues of features oiti,dm and their relationship, like e.g.

tf within-document frequency (wdf) oiti,

logidf — log(inverse document frequency),

lognumterms = log(number of different terms in dm),

imaxtf = l/(maximum wdf of a term in dm)

is-singlt =1, if term is a single word, =0 otherwise

is.phrase =1, if term is a phrase, =0 otherwise.

(As phrases, we considered all adjacent non-stopwords

that occurred at least 25 times in the D1+D2 (training)

document set.)

Based on these relevance descriptions, we estimate

the probability P{R\x{ti,dm)) that an arbitrary term- document pair having relevance description x will be in- volved in a relevant query-document relationship. This

probability is estimated by a so-called indexing function u{x). Difl"erent regression methods or probabilistic

classification algorithms can serve as indexing function.

For our retrieval runs submitted to TREC-2, we used

polynomial regression for developing an indexing function of the form

u{x) = b v{x), (1)

where the components of v[x) are products of elements

of X.

The indexing function actually used has the form

67
u{x) =

bo +

bi issingle tf logidf imaxtf +

62 issingle tf imaxtf +

63 issingle logidf +

64 lognumterms • imaxtf +

65 isjphrase tf logidf imaxtf +

fee is-phrase tf imaxtf +

67 isjphrase • logidf.

The coefficient vector b is computed based on a training

sample of query-document-pairs with relevance judge- ments.

Since polynomial functions may yield results outside the

interval [0, 1], these values were mapped onto the corre- sponding boundaries of this interval. For each phrase occurring in a document, indexing

weights for the phrase as well as for its two components

(as single words) were computed.

There are two major problems with this approach which we are investigating currently:

1. Which factors should be used for defining the in- dexing functions? We are developing a tool that

supports a statistical analysis of single factors for this purpose.

2. What is the best type of indexing function? Pre- vious experiments have suggested that regression

methods outperform other probabilistic classifica- tion methods. As a reasonable alternative to polynomial regression, logistic regression seems to offer some advantages (see also [Fuhr & Pfeifer 94]). As

a major benefit, logistic functions yield only values

between 0 and 1, so there is no problem with outliers. We are performing experiments with logistic

regression and compare the results to those based on polynomial regression.

3 Query term weighting for adhoc queries

3.1 Theoretical background

The basis of our query term weighting scheme for ad-hoc

queries is the linear utility-theoretic retrieval function

described in [Wong & Yao 89]. Let qj denote the set of terms occurring in the query, and the indexing

weight u{x{ti,dm)) (with Uim = 0 for terms ti not oc- curring in dm)- If Cik gives the utility of term ti for the

actual query qk, then the utility of document dm w.r.t. query q^ can be computed by the retrieval function

For the estimation of the utility weights Cik , we applied

two different methods.

As a heuristic approach, we used tf weights (the number of occurrences of the term ti in the query), which

had shown good results in the experiments described in [Fuhr &L Buckley 91]. As a second method, we applied linear regression to this problem. Based on the concept of polynomial retrieval functions as described in [Fuhr 89b], one can estimate

the probability of relevance of qk w.r.t. dm by the for- mula

P(R\qk,dm) ^ ^ Cik -Uim- (3)

If we had relevance feedback data for the specific query

(as is the case for the routing queries), this function

could be used directly for regression. For the ad-hocqueries, however, we have only feedback information

about other queries. For this reason, we regard query

features instead of specific queries. This can be done

by considering for each query term the same features as described before in the context of document indexing.

Assume that we have a set of features {/o, /i, • • •

, /;}

and that xji denotes the value of feature fj for term

ti- Then we assume that query term weight Cjfc can be

estimated by linear regression according to the formula

Cik — I T \ / (4)

Here the factor serves for the purpose of normalization across different queries, since queries with a larger

number of terms tend to yield higher retrieval status val- ues with formula 2. The factors aj are the coefficients that are to be derived by means of regression. Now we

have the problem that regression cannot be applied to eqn 4, since we do not observe Cik values directly. In- stead, we observe relevance judgements. This leads us back to the polynomial retrieval function 3, where we

substitute eqn 4 for Cik:

P{R\qk,dm) « X] I "^"^

with

j = 0

Vj — / J I rj,.XjiUim

(5)

(6)

Q{(lk,dm) = Cik ' ^im- (2) Equation 5 shows that we can apply linear regression

of the form P{R\qk,dm) « a • y to a training sample

68
of query-document pairs with relevance judgements in order to determine the coefficients Oj . The values yj can be computed from the number of query terms, the values

of the query term features and the document indexing

weights. For the experiments, the following parameters were considered as query term features: xo =1 (constant)

xi = if (within-query-frequency)

X2 - \ogtf

X3 =tf- idf

X4 = is-phrase

X5 = inJitle {=1, if term occurs in query title, =0 otherwise) For most of our experiments, we only used the parameter vector X = (xq, , x^)^ . The full vector is denoted

as x' . Below, we call this query term weighting method

reg. This method is compared with the standard SMART

weighting schemes: nnn: Cik = tf

ntc: Cik = tf idf

Inc: dk = {l + \ogtf)

he: Cik = (l + logtf) -idf

3.2 Experiments

In order to have three different samples for learning

and/or testing purposes, we used the following combinations of query sets and document sets as samples:

Q3/D12 was used as training sample for the reg method,

and both Q1/D12 and Q2/D3 were used for testing. As

evaluation measure, we consider the 11-point average

of precision (i.e., the average of the precision values at 0.0,0.1, . . 1.0 recall).

sample

QTW Q1/D12 Q2/D3

nnn 0.2303

ntc 0.2754

Inc 0.2291 0.2601

lie 0.2826 0.2783

reg 0.2698 0.2678

Table 1: Global results for single words

First, we considered single words only. Table 1 shows

the results of the different query term weighting (QTW)

methods. First, it should be noted that the ntc and Itc methods perform better than nnn and Inc. This find- ing is somewhat different from the results presented in [Fuhr & Buckley 91], where the nnn weighting scheme

gave us better results than the ntc method for Isp in- dexing. However, in the earlier experiments, we used

only fairly small databases, and the queries also were

much shorter than in the TREC collection. These facts may account for the different results.

test sample

run Ip^rnincr I^OlLlIlilc. Qamrilp Id<XLHL/M\j 01/012 02/D3

1 every doc. X 0.2698 0.2678

2 every 100. doc. X 0.2700 0.2678

3 every 1000. doc. X 0.2662

4 judged docs only X 0.2635 0.2677

5 every doc. x' 0.2654

6 every 100. doc. x' 0.2677

Table 2: Variations of the reg learning sample and the

query features

run

factor 1 2 3 4 5 6 constant -3.05 -2.96 1.04 -20.80 2.47 -1.71 tf -7.54 -10.67 -2.40 14.58 -4.32 14.11

logi/ -9.01 -5.69 -5.08 -81.58 -1.48 -0.70 tf idf 5.84 7.00 1.63 8.72 1.81 7.70

is-phrase -8.06 -19.23 -2.73 19.81 -2.97 20.39

inJitle -1.70 3.44

Table 3: Coefficients of query regression

In a second series of experiments, we varied the sample size and the set of features of the regression method

(table 2). Besides using every document from the learn- ing sample, we only considered every 100th and every

1000th document from the database, as well as only

those documents for which there were explicit relevance

judgements available. As the results show almost no

differences, it seems to be sufficient to use only a small

portion of the database as training sample in order to save computation time. The additional consideration

of the occurrence of a term in the query title also did not effect the results. So query titles seem to be not

very significant. It is an open question whether or not

other parts of the queries are more significant, so that

the consideration as an additional feature would affect retrieval quality.

The coefficients computed by the regression process for the second series of experiments are shown in table 3.

It is obvious that the coeffients depend heavily on the

choice of the training sample — so it is quite surprising

that retrieval quality is not affected by this factor. The

only coefficient which does not change its sign through

all the runs is the one for the tf idf factor. This seems

to confirm the power of this factor. The other factors can be regarded as being only minor modifications of

the if idf query term weight.

Overall, it must be noted that the regression method

does not yield an improvement over the ntc and Itc methods. This seems to be surprising, since the regresj sion is based on the same factors which also go into the I

69
ntc and Itc formulas. However, a possible explanation

could be the fact that the regression method tries to minimize the quadratic error for all the documents in the learning sample, but our evaluation measure considers at most the top ranking 1000 documents for each

query; so regression might perform well for most of the

documents from the database, but not for the top of

the ranking list. There is some indication for this ex- planation, since regression yields always slightly better

results at the high recall end.

a result 0.00 0.3199

0.10 0.3707

0.15 0.3734

0.20 0.3700

0.25 0.3656

0.30 0.3610

0.50 0.3451

1.00 0.3147

Table 4: Effect of downweighting of phrases (sample

Q2/D12)

As described before, in our indexing process, we consider phrases in addition to single words. This leads to the problem that when a phrase occurs in a document, we index the phrase in addition to the two single words

forming the phrase. As a heuristic method for overcoming this problem, we introduced a factor for downweighting query term weights for phrases. That is, the actual

query term weight of a phrase is c'^^. — acik, where Cik

is the result of the regression process. In order to derive a value for a, we performed a number of test runs with varying values (see table 4). Obviously, weighting

factors between 0.1 and 0.3 gave the best results. For

the official runs, we choose a = 0.15.

sample

QTW a Q1/D12 Q2/D3

lie 0.15 0.3192 0.3131

Itc 0.2 0.3220 0.3056

reg 0.15 0.3080 0.3062

Table 5: Results for single words and phrases

In table 5, this method is compared with the Itc formula,

where we also choose a weighting factor for phrases

which gave the best results. One can see that with the

sample Q2/D3, the differences between the methods are smaller than on sample Q1/D12, but still itc seems to perform slightly better. Finally, we investigated another method for coping with

phrases. For that, let us assume that we have binary

query weights only. Now as an example, the single words

ti and t2 form a phrase ^3. For a query with phrase ^3

(and thus also with ti and ^2); a document dm containig

the phrase would yield + W2m + as value of the

retrieval function, where the weights Uim are computed

by the Isp method described before. In order to avoid

the effect of counting the single words in addition to the phrase, we modified the original phrase weight as

follows: "3m = "3m - "im - U2m

and stored this value as phrase weight. Queries with

the single words or ^2 are not affected by this modification. For the query with phrase ^3, however, the re- trieval function now would yield the value uim + U2m +

"3m — "3m, which is what we would like to get.

QTW a result reg 0.00 0.2724

reg 1.00 0.2596

ntc 0.00 0.2754

ntc 0.15 0.3110

ntc 1.00 0.2524

Table 6: Results for the subtraction method (sample

Q1/D12)

Table 6 shows the corresponding results (o- = 0 means

that single words only are considered). In contrast to what we expected, we do not get an improvement over

single words only when phrases are considered fully.

The result for the ntc method shows that still phrases

should be downweighted. Possibly, there may be an improvement with this method when we would use binary

query term weights, but it is clear that other query term

weighting methods mostly give better results.

3.3 Official runs

As document indexing method, we applied the

description-oriented approach as described in section 2. In order to estimate the coefficients of the indexing function, we used the training sample Q12/D12, i.e. the

query sets Qi and Q2 in combination with the documents from Di and D2.

Two runs with different query term weights were submitted. Run dortL2 is based on the nnn method, i.e. tf weights. Run dortQ2 uses reg query term weights. For performing the regression, we used the query sets Qi

and Q2 and a sample of 400, 000 documents from Di

.

Table 7 shows the results for the two runs (Numbers in parentheses denote figures close to the best/worst results.). As expected, dortQ2 yields better results than

dortL2. The recall-precision curves (see figure 1) show

that there is an improvement throughout the whole recall range. For precision average and precision at 1000

documents retrieved, run dortQ2 performs very well,

while precision at 100 documents retrieved is less good.

This confirms our interpretation from above, saying that

70
"dortL2" -9-

"dortQ2"

—1 I I I 1 I -1-I

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Recall

Figure 1: Recall-precision curves of ad-hoc runs

regression optimizes the overall performance, but not

necessarily the retrieval quality when only the top ranking documents are considered. With regard to the moderate results for the reg query term weighting method,

the good performance of dortQ2 obviously stems from

the quality of our document indexing method.

run dortL2 dortq2

query term weighting nnn reg average precision: Prec. Avg. 0.3151 0.3340

query-wise comparison with median:

Prec. Avg. 37:2 45:4

Prec. @ 100 docs 35:11 34:7

Prec. @ 1000 docs 37:10 45:2 Best/worst results: Prec. Avg. 3/0 3(l)/0

Prec. @ 100 docs 3(2)/l 4(l)/0(2)

Prec. @ 1000 docs 6(l)/0 9(l)/0

dortL2 vs. dortq2:

Prec. Avg. 21:29

Prec. @ 100 docs 22:24

Prec. @ 1000 docs 17:29

Table 7: Results for adhoc queries

4 Query term weighting for

routing queries

4.1 Theoretical background

For the routing queries, the retrieval-with-probabilisticindexing (RPI) model described in [Fuhr 89a] was applied. The corresponding retrieval function is based on

the following parameters:

Uim indexing weight of term ti in document dm

set of documents judged relevant for query qk, Pik expectation of the indexing weight of term ti in D^,

set of documents judged nonrelevant for query qk,

rik expectation of the indexing weight of ti in . The parameters pik and rik can be estimated based on

relevance feedback data as follows:

Pik Dl

Then the query term weight is computed by the formula

Cik rik(i - Pik)

1

71
='dortVl"

"dortPl"

—I

1 1——

r

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Recall

Figure 2: Recall-precision curves of routing runs

and the RPI retrieval function yields

Q{qk,dm) = ^ \og{CikUim + 1). (7)

4.2 Experiments

In principle, the RPI formula can be applied with

or without query expansion. For our experiments in TRECl, we did not use any query expansion. The

final results showed that this was reasonable, mainly

with respect to the small amount of relevance feed- back data available. In contrast, for TREC2 there were about 2000 relevance judgements per query, so there was

clearly enough training data for applying query expansion methods.

As basic criterion for selecting the expansion terms, we

considered the number of relevant documents in which a term ocurs, which gave us a ranking of candidates; document indexing weights were considered for tie-breaking.

Then we varied the the number of terms which are added

to the original query.

expansion result

0 0.2909

10 0.3047

30 0.3035

50 0.3002

100 0.2832

Table 8: Effect of number of expansion terms

In a first series of experiments, we considered single

word only. We used Q2/D1 {bp document indexing)

as training sample and Q2/D2 {Itc indexing) as test

sample. As can be seen from table 8, query expansion

clearly improves retrieval quality, but only for a limited

number of expansion terms. For larger numbers, we get worse results. This effect seems to be due to parameter

estimation problems.

expansion

phraseweight single w. phrases result 0.5 0 0 0.3476

0.5 20 0 0.3713

1.0 20 0 0.3730

0.5 20 10 0.3728

1.0 20 10 0.3605

0.5 30 10 0.3729

1.0 30 10 0.3626

Table 9: Query expansion with phrases

In a second series of experiments, we looked at the

combination of single words and phrases. These ex- periments were performed as retrospective runs, with

Q2/D12 as training sample and Q2/D2 as test sample

(both with Itc document indexing). For the number of

expansion terms, we treated single words and phrases

separately. Furthermore, similar to the adhoc runs, we

used an additional factor for downweighting the query

term weights of phrases. The different parameter combinations tested and the corresponding results are given

in table 9. Obviously, phrases as expansion terms gave no improvement, so we decided to have only single words

as expansion terms (but the phrases from the original

query still are used for retrieval). Furthermore, the re- trieval quality reaches its optimum at about 20 terms.

72
4.3 Official runs

Two different runs were submitted for the routing

queries, both based on the RPI model.

Run dortPl uses the same document indexing function

as for the adhoc queries. Query terms were weighted

according to the RPI formula. In addition, each query was expanded by 20 single words. Phreises were not downweighted.

Run dortVl is based on Itc document indexing. Here no query expansion took place.

run dortVl dortPl

document indexing Itc Isp

query expansion none 20 terms

average precision: Prec. Avg. 0.3516 0.3800

query-wise comparison with median:

Prec. Avg. 38:10 46:4

Prec. @ 100 docs 31:11 40:5

Prec. @ 1000 docs 32:9 37:7 Best/worst results: Prec. Avg. 1/0 4(2)/0

Prec. @ 100 docs 3(3)/l(l) 7(5)/l(l)

Prec. @ 1000 docs 6(2)/0(l) 10(2)/0(1)

dortVl vs. dortPl:

Prec. Avg. 10:39

Prec. @ 100 docs 9:27

Prec. @ 1000 docs 7:33

Table 10: Results for routing queries

Table 10 shows the results for the two runs. The recall- precision curves are given in figure 2. Again, the results confirm our expectations that LSP indexing and query

expansion yields better results.

5 Conclusions and outlook

procedures, this combination seems to be a prospective

area of research. A Operational details of runs

A.l Basic Algorithms

The algorithm A to find the coefficient vector a for the

ad-hoc query term weights can be given as follows:

Algorithm A

1 For each query document pair {qk,dm) G

{Q\ U Q2) X D3 with D, being a sample from

(Di U D2) do

1.1 determine the relevance value of

the document dm with respect to the

query qk- 1.2 For each term ti occuring in qk do

1.2.1 determine the feature vector Xi and the indexing weight Uim of the

term /j w.r.t. to document dm1.3 For each feature j of the feature vectors X compute the value of yj looping over

the terms of the query.

1.4 Add vector x and relevance value rkm

to the least squares matrix.

2 Solve the leeist squares matrix to find the coefficient vector a The algorithm B to find the coefficient vector b for the

document indexing is sketched here:

Algorithm B

1 Index Di [JD2 (the learning document set) and

Qi U Q2 (the learning query set).

The experiments described in this paper have shown

that probabilistic learning approaches can be applied

successfully to different types of indexing and retrieval. For the ad-hoc queries, there seems to be still room for further improvement in the low recall range. In order to increase precision, a passage-wise comparison of query

and document text should be performed. For this purpose, polynomial retrieval functions could be applied. In the case of the routing queries, we first have to inves- tigate methods for parameter estimation in combination

with query expansion. However, with the large number

of feedback documents given for this task, other types

of retrieval models may be more suitable, e.g. queryspecific polynomial retrieval functions.

Finally, it should be emphasized that we still use rather

simple forms of text analysis. Since our methods are

flexible enough to work with more sophisticated analysis

2 For each document d ^ Di U D2

2.1 For each g G Qi U Q2

2.1.1 Determine the relevance value r of

d to q 2.1.2 For each term t in common be- tween (set of query terms) and

(f^ (set of document terms)

2.1.2.1 Find values of the ele- ments of the relevance

description involved in

this run and add values

plus relevance information to the least squares

matrix being constructed

3 Solve the least squares matrix to find the coefficient vector b

73
The algorithm C to index a document set D can now

be given as:

Algorithm C

1 For each document d £ D

1.1 For each term t £ (f^ 1.1.1 Find values of the relevance de- scription x{t,d) involved in run.

: ' 1.1.2 Give i the weight 6

-

1.1 Add d to the inverted file.

A. 2 Ad-hoc runs

The algorithm D is used for indexing and retrieval for the ad-hoc runs. Steps numbered with a trailing "A"

apply only for run dortQ2, steps with trailing "B" only

to run dortL2.

Algorithm D

1 Run algorithm B to determine the coefficient vector b for document indexing.

lA Run algorithm A to determine the coefficient vector a for query indexing.

2 Call algorithm C for document set Di U

3 For each query qk G Q3 do

3.1 For each term U occuring in qk do

3. 1.1 A Determine the feature vector Xik and compute the query term

weight Cik by multiplying it to a. 3.1. IB Weight w.r.t. qk (test query

set) with tf weights (nnn variant).

Phrases where downweighted by

multiplying the weights with a =

0.15. 3.2 Run an inner product inverted file similarity match of cjb against the inverted

file formed in step 2, retrieving the top

1000.

Algorithm E

lA Index query set Q2 and document set Di U D2

with tf idf weights.

IB Index query set Q2 and document set Di U D2

by calling algorithm C

2 For each query g G Q2

2.1 For each term t E q^ (set of query terms)

2.1.1 Reweight term t using the RFI rel- evance weighting formula andthe

relevance information supplied.

3A Index document set D3 by calling algorithm C. 3B Index document set D3 with tf idf weights.

Note that the collection frequency information

used was derived from occurrences in D\ U D2

only (in actual routing the collection frequencies

within D3 would not he known).

4 Run the reweighted queries of Q2 (step 2)

against the inverted file (step 3), returning the

top 1000 documents for each query.

References

Fuhr, N.; Buckley, C. (1991). A Probabilistic Learning Approach for Document Indexing. ACM Transactions on Information Systems 9(3), pages 223-248.

Fuhr, N.; Buckley, C. (1993). Optimizing Document

Indexing and Search Term Weighting Based on Probabilistic Models. In: Harman, D. (ed.): The First Text REtrieval Conference (TREC-1), pages 89-100.

National Institute of Standards and Technology Special Publication 500-207, Gaithersburg, Md. 20899.

Fuhr, N.; Pfeifer, U. (1994). Probabilistic Information Retrieval as Combination of Abstraction, Inductive Learning and Probabilistic Assumptions. ACM

Transactions on Information Systems 12(1).

Fuhr, N. (1989a). Models for Retrieval with Probabilistic Indexing. Information Processing and Management 25(1), pages 55-72.

Fuhr, N. (1989b). Optimum Polynomial Retrieval

Functions Based on the Probability Ranking Principle. ACM Transactions on Information Systems 7(3),

pages 183-204.

Wong, S.; Yao, Y. (1989). A Probability Distribution Model for Inforrnation Retrieval. Information

Processing and Management 25(1), pages 39-53.

A.3 Routing runs

Algorithm E is used for indexing and retrieval for the

routing runs. Steps numbered with a trailing "A" apply

only for run dortPl, steps with trailing "B" only to run

dortVl..

74
TREC-2 Routing and Ad-Hoc Retrieval Evaluation using

the INQUERY System

Bruce Croft, James Callan, and John Broglio

Computer Science Department

University of Massachusetts

Amherst, MA. 01003

1 Project Goals

The ARPA TIPSTER project, which is the source of the data and funding for TREC,

has involved four sites in the area of text retrieval and routing. The TIPSTER project

in the Information Retrieval Laboratory of the Computer Science Department, University

of Massachusetts, Amherst (which includes MCC as a subcontractor), has focused on the

following goals:

• Improving the effectiveness of information retrieval techniques for large, full-text databases,

• Improving the effectiveness of routing techniques appropriate for long-term information needs, and

• Demonstrating the effectiveness of these retrieval and routing techniques for Japanese

full text databases [4]. Our general approach to achieving these goals has been to use improved representations

of text and information needs in the framework of a new model of retrieval. This model

uses Bayesian networks to describe how text and queries should be used to identify relevant

documents [6, 3, 7]. Retrieval (and routing) is viewed as a probabilistic inference process

which compares text representations based on different forms of linguistic and statistical

evidence to representations of information needs based on similar evidence from natural

language queries and user interaction. Learning techniques are used to modify the ini- tial queries both for short-term and long-term information needs (relevance feedback and

routing, respectively).

This approach (generally known as the inference net model and implemented in the

INQUERY system) emphasizes retrieval based on combination of evidence. Different text representations (such as words, phrases, paragraphs, or manually assigned keywords) and

different versions of the query (such as natural language and Boolean) can be combined

in a consistent probabilistic framework. This type of "data fusion" has been known to be

effective in the information retrieval context for a ntmiber of years, and was one of the

primary motivations for developing the inference net approach.

Another feature of the inference net approach is the ability to capture complex structure

in the network representing the information need (i.e. the query). A practical consequence

75
of this is that complex Boolean queries can be evaluated as easily as natural language queries

and produce ranked output. It is also possible to represent "rule-based" or "concept-based"

queries in the same probabilistic framework. This has led to us concentrating on automatic

analysis of queries and techniques for enhancing queries rather than on in-depth analysis

of the documents in the database. In general, it is more effective (as well as efficient) to

analyze short query texts than millions of document texts. The results of the query analysis are represented in the INQUERY query language which contains a ntmiber of operators,

such as #SUM, #AND, #0R, #NOT, #PHRASE, and #SYN. These operators implement

different methods of combining evidence and describing concepts.

Some of the specific research issues we are addressing are morphological analysis in English and Japanese, word sense disambiguation in English, the use of phrases and other

syntactic structure in English and Japanese, the use of special purpose recognizers (for

example, company, country and people name recognizers) in representing docimients and

queries, analyzing natural language queries to build structured representations of information needs, learning techniques appropriate for routing and structiired queries, techniques

for acquiring domain knowledge by corpus analysis, and probability estimation techniques

for indexing.

The first TREC evaluation and the two previous TIPSTER evaluations have made it clear that a lot remains to be learned about retrieval in large, full-text databases based on complex information needs. Issues as phrases, relevance feedback, and probability es- timation have proven to be quite difficult in such environments. On the other hand, the

effectiveness levels achieved have been quite good. The experiments done in the TREC2 evaluation, together with the 24 month TIPSTER evaluation which followed it, were

designed to improve our understanding about which IR techniques work and why.

2 System Description

The document retrieval and routing system that has been developed on the basis of the in- ference net model is called INQUERY [2]. The main processes in INQUERY are document

indexing, query processing, query evaluation and relevance feedback.

In the document indexing process, dociraients axe parsed and index terms representing

the content of documents are identified. INQUERY supports a variety of indexing techniques including simple word-based indexing, indexing based on part-of-speech tagging and

phrase identification, and indexing by domain- dependent features such as company names,

dates, locations, etc. The last type of indexing is a first step towards integrating detection

and extraction systems.

In more detail, the document structure is used to identify which parts wiU be used for

indexing. The first step of this process is then to scan for word tokens. Most types of

words (including numbers) are indexed, although a stopword list is used to remove very

coHMnon words. Stopwords can be indexed, however, if they are capitalized (but not at the start of sentences) or joined with other words (e.g. "the The-1 system"). Words are then stemmed to conflate variants. Although the Porter stemmer was used for the TREC-2

76
experiments, we have developed a new stemming algorithm that has a number of advantages

for operational systems. A number of recognizers written in flex are then used to identify

objects such as company names and mark their presence in the document using "meta"

index terms. A company name such as IBM in the text, for example, will restdt in a meta

term ^COMPANY being recorded at that position in the text. The use of these meta terms

extends the range of queries that can be specified. This completes the usual processing for

docimient text. The doctraient indexing process also involves building the compressed inverted files that are necessary for efficient performance with very large databases. Since positional

information is stored, overhead rates axe typically about 40% of the original database size. The query processing process involves a series of steps to identify the important concepts

and structure describing a user's information need. INQUERY is unique in that it can

represent and use complex structured descriptions in a probabihstic framework. Many of

the steps in query processing are the same as those done in document indexing. In addition, a part-of-speech tagger is to used to identify candidate search phrases. Domain-dependent

featiires are recognized and meta-terms inserted into the query representation. The relative

importance of query concepts is also estimated, and relationships between concepts axe suggested based on simple grammar rtdes. An evaluation of some of the query processing

techniques is presented in [1]. INQUERY also has the capability of expanding the query using relationships between

concepts found by either using manually specified domain knowledge in the form of a simple

thesaurus or by corpus analysis. The WORDFINDER system is a version of INQUERY

that retrieves concepts that are related to the query. WORDFINDER is constructed by

identifying noun groups in the text and representing them by the words that are closely

associated with them (i.e. occiir in the same text windows). Concept "documents" axe then

stored in INQUERY. This technique of query expansion was not tested in TREC-2.

The query evaluation process uses the inverted files and the query represented as an

inference net to produce a document ranking. The evaluation involves probabilistic inference

based on the operators defined in the INQUERY language. These operators define new

concepts and how to calculate the belief in those concepts using linguistic and statistical

evidence. We are constantly experimenting with and refining these operators (for example,

the operator defining a phrase-based concept) in order to improve retrieval performance.

The relevance feedback process uses information from user evaluations of retrieved documents to modify the original query in detection or routing environments. The INQUERY

system, because it can represent structured queries, supports a wide range of learning techniques for query modification [5]. In general, new words and phrases axe identified in the

sample of relevant documents. These axe added to the original query and all the terms

in the query are then reweighted. With the amount of relevance information available in

TIPSTER, relatively simple automatic techniques appear to produce good levels of effec- tiveness. We axe also investigating the effiect of using more limited information and more

complex learning techniques, such as neural networks.

77
3 Query Processing

In order to claxify the query processing done for the TREC and TIPSTER experiments

with INQUERY, the following sections give more detailed descriptions.

There are two main kinds of query styles: a natural language query and a keyword or key concept query. For example, the <desc> and <narr> fields of a TIPSTER query

represent natural language queries of varying levels of abstraction. The <con>, <title>

and <fac> fields represent key concepts in the query. The main difference between the

two types of processing is that the key concept query has more controlled information.

The phrasing and emphasis axe already given and do not have to be conjectured from the

language structiire. It is valuable to discover how to treat both styles of query, because a good user interface will maJce it easy for a user to input both styles. For example, a user may enter a prose query and then highlight the important words and phrases in the query

in some convenient manner. These highlighted words would then be treated as key concepts

in the query processing.

3.1 Prose query processing

Natural language query fields are tagged for syntactic category by a part-of-speech (POS)

tagger. Currently we use the tagger developed by Ken Church. We have developed our own POS tagger, and we expect to begin using it in the fall of 1993. There are some pretagging and post-tagging "housekeeping" operations, such as removing parentheses. (The

current version of INQUERY does not permit parentheses except as part of an operator,

and we do not yet make any inferences from the presence of parentheses during the text processing.) Additionally, we change operator phrases to single words in order to simplify

later processing. An example of this simplification is replacing the phrase in order to with

the infinitive particle to or replacing with respect to with the word regarding. The goal of

this replacement is to remove phrases which resemble noim phrases syntactically but which

are reaUy syntactic operators (e.g., phrasal prepositions) with no substantive content. At

this stage, stop phrases are also removed.

3.1.1 Noun and adjective phrase capture: orthographic and syntactic clues.

When the text is tagged and the potentially irrelevant material has been removed, syntacticaUybased noim group capture is performed. Certain kinds of noun phrase patterns are enfolded

in a #PHRASE operator:

1. A noun phrase which contains more than one modifying adjective and noim is enclosed

in a #PHRASE operator;

2. A head noim with no premodifiers and followed by a prepositional phrase is enclosed

in a :^PHRASE operator with the head noun of the prepositional phrase;

78
3.1.2 Constraint capture

All text in the query is seaxched for constraint expressions. Among these expressions are the words company, not U. S. or a restriction in the nationaUty section of the <fac> field to U.S. or other nationality. A restriction to U.S. nationality as the area of interest is implemented by penalizing documents for references to foreign cotmtries. A restriction to other nationalities is implemented by repeating that country as a term. This asymmetry

depends on the fact that the document collection is drawn solely from U.S. sources, and

therefore the U.S., as the default area of interest, is rarely referred to imless the government

or foreign policy implementation is under discussion.

There is some recognition of simple time expressions, such as since 1984 which are expanded to the set of years which might be intended by the phrase in question.

Countries are recognized as such and are handled so that expressions like South Africa

are phrased as #1 ( south africa ) even when they appear in the middle of a larger group

of capitalized words. In addition, proper names such as country names are moved out of

the scope of #PHRASE operators, since it generally increases the effectiveness of a #PHRASE to reduce the number of words in it. Nationality constraints can better be maintained within

the scope of the larger and more tolerant #SUM operator. For example the phrase

"import ban on South African diamonds"

becomes by stages,

#PHRASE (import ban on #SYN (#1 (south african) #1 (south africa)) diamonds)

and finally

#SUM (#SYN (#1 (south african) #1 (south africa))

#PHRASE( import ban on diamonds)).

3.2 Key concept query processing

Key concept query processing is different from prose query processing since the concept

separation provided by the user can presumably be trusted. Instead of using a part-ofspeech tagger, we rely on comma delimitation of concepts, and ^PHRASE the words found

between each pair of delimiters.

Additionally, if any constraints were foimd anywhere else in the query, e.g., a mention of

the word company or an exclusionary geographical constraint (e.g., not USA or only USA),

the query wiU be modified according to these constraints. For example,

only USA #NOT (#FOREIGNCOUMTRY ) and

not USA #NOT ( #USA )

.

If the word company is found in a query, then a second copy of the key concepts (the

<con> field), is produced where each item in the field appears in an unordered window

operator with the special concept :j^COMPANY. For example, if the word South Africa

appears as a key concept (and company appears somewhere in the query), then the pre- processor would produce the term #UW50( #COMPANY #1( south africa)) which would

match any docimaent which had a company name within fifty words of South Africa.

79
4 The TREC Experiments

Four experiments were submitted to the TREC evaluation, two "ad-hoc" and two "routing".

In these experiments, we emphasized automatic query processing and automatic feedback

algorithms for routing. The following is a summary:

, • AdHoc: topics 101-150 against TIPSTER volumes 1 and 2. INQOOl Created automatically from TIPSTER topics. Contains phrases. Details of

query processing used are described below.

INQ002 INQOOl queries, modified manually. Modifications restricted to eliminating

words and phrases, and adding paragraph-level operators around existing words

and phrases. The method for doing this was done somewhat differently than last year's TREC conference, as discussed below.

• Routing: topics 51-100 against TIPSTER volume 3. INQ003 Created automatically from TIPSTER topics and relevance judgements

from Volumes 1 and 2. Baseline queries (from a previous TIPSTER evaluation) were modified by reweighting and adding single-word terms. The term

weighting and selection function used was df.idf, as described in [5]. Only the

top 120 relevant documents found by INQUERY were used for feedback, and 30

terms were added to each query.

INQ004 Formed by combining (using the #SUM operator) INQOOl queries and INQRYP queries (used in TIPSTER 18 month evaluation). The INQRYP queries were produced automatically and then modified manually. Modifications re- stricted to eliminating words and phrases, and adding paragraph-level operators

around existing words and phrases.

Query Type Average Precision

5 Docs 30 Docs 100 Docs 11-Pt Avg

INQOOl .62 .57 .49 .36 INQ002 .60 (-2.6%) .59 (+3.5%) .51 (+4.1%) .36 (0%)

Table 1: Results for Adhoc queries

Table 1 gives the results for the adhoc queries. These show that there is little difference

in effectiveness between the automatically processed queries and the semi-automaticaJly

processed queries. The query processing for the automatically processed queries has been

significantly improved as described in the previous section, but there is another effect. Compared to the manual query run in the last TREC conference, paragraph-level concepts were formed in a much more mechanistic way and were constrained by the language of the

description and the narrative. In the previous conference, the only constraint was the vo- cabulary used in the queries, and the user's "world knowledge" was used to group concepts.

80
This resulted in considerably better retrieval performance. Additional experiments using

manually edited queries are discussed in the next section.

Query Type Average Precision

5 Docs 30 Docs 100 Docs 11-Pt Avg

INQ003 .64 .56 .45 .35 INQ004 .67 (+3.7%) .58 (+2.7%) .45 (0%) .36 (+2.4%)

Table 2: Results for Routing queries

The routing results show that some improvement is obtained by combining the manual

queries with the queries that were automatically modified using relevance feedback techniques. The difference in performance between the two types of queries is considerably less than last year, however. Our own experiments have also shown that no additional gains in

performance were obtained by using more than the top 150 documents from the INQUERY

output. This is a significant result from a practical viewpoint, since in an operational environment we will not want to rely on having output from other systems or need thousands

of relevance judgements before performance improves.

5 Other Experiments

In the TIPSTER 24 month evaluation, which took place soon after the TREC-2 evaluation,

we did a number of experiments that complement those done in TREC. In paxticular, we

evaluated paragraph-based retrieval, expansion using an automatically generated thesaurus,

feedback techniques that use phrases, and Japanese indexing techniques. In this section, we

report some of the most interesting results. The precision figures given here are calculated

using the TREC-2 relevance judgements, rather than the TIPSTER judgements.

The first two experiments were with adhoc queries. INQ041 (the ntimbers are consistent

with those used in TIPSTER and other publications) is a run that used a different manually

modified version of INQOOl. That is, the manual modifications were the same as those done

in the first TIPSTER and TREC evaluations, rather than the more restricted modifications

done for INQ002. INQ042 is a run that combines INQ041 with INQOOl.

Query Type Average Precision

5 Docs 30 Docs 100 Docs 11-Pt Avg

INQ041 .68 .60 .50 .36 INQ042 .65 (-4.6%) .61 (+1.7%) .51 (+2.0%) .38 (+5.6%)

Table 3: Results for TIPSTER adhoc queries

These results show that the manually modified queries can achieve significantly better

precision at low recall levels. For example, at the 5 document cutoff level, the average

precision for INQ041 is 9.7% higher than INQOOl. The overall average is the same, however.

This is a much smaller difference than was seen in the first TREC and TIPSTER evaluations

81
of the INQUERY system and it indicates that the automatic query processing has improved

considerably.

The combination search (INQ042) is slightly worse than INQ041 at the 5 dociraient

cutoff level, but overall is better than either the automatic or manual queries on their own. An adhoc search that incorporates automatic paragraph-level matching was also tested in

TIPSTER and this resulted in a further 5% improvement.

E*fQ023 and INQ024 are routing query sets that were created automatically using rel- evance judgements from volumes 1 and 2. In addition to the single-word terms added in

INQ003, 10 phrase-level concepts and 20 paragraph-level concepts were added to the query.

A phrase-level concept is a #UW5 two- word pattern that occurs frequently in the relevant

documents, and a paragraph-level concept is a t^^UWSO two- word pattern. The #UWn

operator looks for co-occurrence in any order in a text window of size n. The difference

between INQ023 and INQ024 is that INQ023 contains the original query terms in addition

to terms extracted from relevant docvunents, whereas INQ024 contains only terms from

relevant documents.

Query Type Average Precision

5 Docs 30 Docs 100 Docs 11-Pt Avg

INQ023 .67 .60 .47 .38

INQ024 .68 (-f 1.5%) .59 (-1.7%) .46 (-2.2%) .39 (+2.6%)

Table 4: Results for TIPSTER routing queries

These residts show that there is little difference between using the original query or just

the relevant documents. This is probably due to the large nimiber of relevance judgements

available in this routing experiment. In a relevance feedback situation, where there are fai

fewer relevant documents, the original query is very important. It is clear that the addition

of phrase and paragraph-level structure to the routing has improved performance. The

average precision for INQ023 is 8.6% higher than INQ003. Combining these new rims with

manually modified routing queries produced further improvements.

6 Summary

The TREC-2 runs, both in the adhoc and routing categories, provided further evidence that

manually generated queries are not, in general, superior to automatically processed natural

language queries. In the case of routing, in fact, the manual queries are significantly less effective. They do, however, improve the effectiveness of retrieval when used in combination

with the automatic queries. This combination of query types has been a theme of the

research at the University of Massachusetts and has been estabUshed as effective in a number

of experiments.

The additional TIPSTER rims showed that learning structure in the form of phrases

and paragraph-level co-occurrences is effective for routing. They also showed that learning

techniques significantly improve performance (the best routing rims were more than 20%

82
higher in terms of average precision than the best queries that were not modified using

relevance judgements). It is becoming apparent that techniques that may not work well in

relevance feedback situations with few identified relevajit documents, may be very effective

in routing where there are many more relevant doctiments identified. We are currently doing

experiments with different forms of weighting, including the use of identified non-relevant

documents.

With regard to improving the performance of adhoc queries, we are continuing to carry

out experiments with different ways of estimating the probabilities (or tf.idf weights) needed

for the inference net, and with different forms of paragraph-level matching. Finally, as mentioned earlier, we have seen some significant improvements using automatic query expansion

based on corpus analysis.

References

[1] J. P. Callan and W.B. Croft. An evaluation of query processing strategies using the TIPSTER collection. In Proceedings of ACM SIGIR International Conference on Research

and Development in Information Retrieval, pages 347-356, 1993.

[2] J. P. CaUan, W.B. Croft, and S.M. Harding. The INQUERY retrieval system. In Proceedings of the 3rd International Conference on Database and Expert Systems Applications,

pages 78-83, 1992.

[3] W. Bruce Croft and Howard R. Turtle. Text retrieval and inference. In P. Jacobs, editor,

Text-Based Intelligent Systems, pages 127-156. Lawrence Erlbaum, 1992.

[4] Hideo Fujii and W. Bruce Croft. A comparison of indexing techniques for Japanese text

retrieval. In Proceedings of the ACM SIGIR International Conference on Research and

Development in Information Retrieval, pages 237-246. ACM, 1993.

[5] David Haines and W. Bruce Croft. Relevance feedback and inference networks. In

Proceedings of the ACM SIGIR International Conference on Research and Development

in Information Retrieval, pages 2-11. ACM, 1993.

[6] H.R. Turtle and W.B. Croft. Evaluation of an inference network-based retrieval model.

ACM Transactions on Information Systems, 9(3):187-222, 1991.

[7] H.R. Turtle and W.B. Croft. A comparison of text retrieval models. Computer Journal,

1992. To appear.

83

DR-LINK: A System Update for TREC-2

Elizabeth D. Liddy

Simg H. Myaeng

School of hrformation Studies

Syracuse University

Syracuse, New York 132100-4100

liddy@mailbox.syr.edu; shmyaeng@mailbox.syr.edu

1

.

Overview of DR-LD*JK's Approach

The theoretical goal underlying the DR-LINK System is to represent and match documents and queries at the various

linguistic levels at which human language conveys meaning. Accordingly, we have developed a modular system

which processes and represents text at the lexical, syntactic, semantic, and discourse levels of language. Li concert, these levels of processing permit DR-LINK to achieve a level of intelhgent retrieval beyond more traditional approaches. In addition, the rich annotations to text produced by DR-LINK are replete with much of the semantics

necessary for document extraction.

The system was planned and developed in a modular fashion and functional modularity has been achieved, while a fuU integration of these multiple levels of linguistic processing is within reach. As currently configured, DR-LINK

performs a staged processing of documents, with each module adding a meaningful annotation to the text. For

matching, a Topic Statement undergoes analogous processing to determine its relevancy requirements for documents

at each stage. Among the many benefits of staged processing are: improvements and changes can be easily made

within any module; the contribution of the various stages can be empirically tested by simply turning them on or

off; modules can be re-ordered (as was done within the last six months) in order to utilize document annotations in various ways, and; individual modules can be incorporated in other evolving systems.

The purpose of each of the processing modules will be briefly introduced here (also see Figure 1) in the order in which the system is currently run, with fuller explanations provided in the section below: 1) the Text Structurer

labels clauses or sentences with a text-component tag which provides a means for responding to the discoiu^ level Topic Statement requirements of tune, source, mtentionality, and state of completion; 2) the Subject Field Coder

provides a subject-based, summary-level vector representation of the content of each text; 3) the Proper Noun

Interpreter and 4) the Complex Nominal Phraser provide precise levels of content representation in the form of

concepts and relations, as well as controlled expansion of group noims and content-bearing nominal phrases; 5) the Relation-Concept Detector produces concept-relation-concept triples with a range of semantic relations expressed via

various syntactic classes, e.g. verbs, nominalized verbs, complex nominals, and proper nouns; 6) the Conceptual

Graph Generator combines the triples to form a CG and adds Roget International Thesaurus (RET) codes to concept

nodes, and; 7) the Conceptual Graph Matcher determines the degree of overlap between a query graph and graphs of

those documents which surpass a statistically predetermined criterion of likelihood of relevance based on ranking by

the integrated processing of the first four system modules.

2. Detailed System Description

In the following system description, emphasis is placed on work accomplished within the last year, plus a basic

overview description of each module. The more rudimentary processing details of each module plus fuller description

of earUer development are available ui the TREC-1 Proceedings (Harman, 1993).

2. A Text Structurer

Since human interpretation of text is influenced by expectations regarding the text to be read, discourse level analysis

is required for a system to approximate the same level of meaningful representation and matching. DR-LINK's Text

Structurer is based on discourse linguistic theory which suggests that texts of a particular type have a predictable

85
DOCUMENTS TOPICS

Preprocessor

i Subject

\ Field

i Coder

V8SFC

Matcher

Ranked

List

Proper Noun (PN)

Interpreter

Complex Nominal (CN)

Phraser

TS+PN+CN

Inverted File

Creator

NLP

I

Query

Constructor

w

Relation Concept

Detector

TS+PN+CN

Matcher

Integrated

Matcher

Recall

Topic CG

Processor

Predictor Matcher

:'»c««c-:-:->:-:o:-x->>:->:-:-x-:-:-:->:-x^^^^

Ranked

List

Ranked

List

Fig. 1:DR-LE>«C System

86
text-level structure which is used by both producers and readers of that text-type as an indication of how and where

certain information endemic to that text-type will be conveyed. We have implemented a Text Structurer for the newspaper text-type, which produces an annotated version of a news article in which each clause or sentence is tagged

for the specific slot it instantiates in the news-text model, an extension of van Dijk's earlier model (1988). The

structure annotations are used to respond more precisely to information needs expressed in Topic Statements, where some aspects of relevancy can only be met by understanding a Topic Statement's discourse requirements. For

example. Topic Statement 75, states that:

Document will identify an instance in which automation has clearly paid off, or conversely,

hasfailed.

which contains the implicit discourse reqtiirement tiiat relevant instances should occur in tiie CONSEQUENCE

component of a news article. DR-LINK extracts this requirement from the Topic Statement and will only assign a

similarity value for the discourse-level of relevance to those documents in which the sought information occurs in a CONSEQUENCE component.

The current news-text model consists of thirty-eight recognizable components of information observed in a large

sample of training texts (e.g. MAIN EVENT, VERBAL REACTION, EVALUATION, FUTURE

CONSEQUENCTE, PREVIOUS EVENT). The Text Structurer assigns these component labels to document clauses

or sentences on the basis of lexical clues learned from text, which now comprise a special lexicon. We considered

expanding the lexicon via available lexical resources such as Roget's International Thesaurus or WordNet. but our

analysis of these resources suggested that they do not capture the particularities of lexical usage in the sublanguage

of newspaper reporting.

The Text Structurer has recentiy been improved to assign structural tags at the clause level, a refinement which has

corrected most of the anomalies that were observed in earlier testings of the Text Structurer. For example, given the new clause-level structuring, the following sentence is conectiy interpreted as containing both future-oriented information in the LEAD-FUTURE segment and some nested information regarding a past situation in the LEADHISTORY segment.

<LEAD-FUT> South Korea's trade surplus, <LEAD-HIST> which more than doubled in 1987

to $6.55 billion, </LEAD-inST> is expected to narrow this year to above $4 billion. </LEADFUT>

We have recentiy implemented new matching techniques which more fully realize the Text Structmer's potential

contribution to the system's performance. This was achieved as one outcome of a study which greatiy increased our

understanding of how text structure requirements in Topic Statements should be used for matching documents to Topic Statements. Analysis of relevant and non-relevant documents retrieved for a test sample of Topic Statements

indicated tiiat most of the errors in tiie Text Structurer's matching were not serious errors, but only sUght

mismatches in terms of die conceptual definitions of some of the text model's components. This suggested that our model was overly specific for the task of responding to discourse aspects of information requirements, and that matching Text Structure needs frcMn a Topic Statement to structured documents called for a more generahzed model.

That is. Topic Statement text-structure requirements are not expressed at the same level of specificity at which Text

Structure components are recognizable in documents.

Given this, we reduced the matching complexity via a function tiiat maps the thirty-eight news-text components to seven meta-components. These are: LEAD-MAIN, fflSTORY, FUTURE, CONSEQUENCE, EVALUATION,

ONCjOING, and OTHERS. The new approach allows the system to continue to impose the finer-level, 38- ccmponent structure on the newspaper articles themselves with excellent precision, but maps this fuller set of text components to the seven meta-components at the matching stage, as the Topic Statements' text structure requirements are coded at the meta-component level. Unofficial experimental results indicate that this new scheme

has significantiy increased the Text Structurer's contribution to an improved level of precision in the retiieval of

relevant documents.

87
While the Text Structurer mcxlule processes documents as described above, the analysis of Topic Statements for their Text Structure requirements is done by the Natural Language Query Constructor (QC) which also analyzes the proper noun and complex nominal requirements of Topic Statements. The QC, as well as the matching and ranking of

documents using these sources of linguistic information, is described below.

2.B. Subiect Field Coder

The Subject Field Coder (SFC), as reported at TElEC-1, has been producing consistendy reliable semantic vectors to represent both documents and Topic Statements using the semantic codes assigned to word senses in a machinereadable dictionary. Details regarding this process are reported in detail in Liddy et al, 1993. Our more recent efforts on this module have focused on multiple ways to exploit SFC-based similarity values between document and query

vectors. One implementation is the use of the ranked vector-similarity values for predicting a cut-off criterion of

potentially relevant documents when the module is used as an initial filter. This is to replace the earlier practice used

in the eighteenth month TIPSTER testing, where documents were ranked by their SFC-vector similarity to a query

SFC-vector and the top two thousand documents were passed to the CG Matcher, since CG matching is too computationally expensive to handle all documents in the collection. To report the SFC's performance, at that time we reported how far down the ranked list of documents the system would need to process documents in order to get

all the judged relevant documents. Although the results were highly promising (all relevant documents were, on

average, in the top 37% of the ranked list based on SFC similarity values), this figure varies considerably for individual Topic Statements. Therefore, we needed to devise a method for predicting a priori for individual Topic

Statements, the cut-off criterion for any desired level of recall. We first developed a method that could successfully

predict a cut-off criterion based on just SFC similarity values. We then extended the algorithm to incorporate the

similarity values produced when proper noun, complex nominal, and text structure requirements are considered as well, to produce an integrated ranking based on these varied sources of linguistic information.

The SFC-based cut-off criterion uses a multiple regression formula which was developed on the odd-numbered Tq)ic

Statements from 1 to 50 and a training corpus of Wall Street Journal articles. The regression formula takes into account the distribution of similarity values for documents in response to a particular query by incorporating the mean and standard deviation of the similarity value distribution, the simiiaxity of the top-ranked document, and the desired recall level. The cut-off criterion was tested on the held-out, twenty-five Topic Statements. The averaged

results, when a user is striving for 100% recall, showed that only 39.65 % of the 173,255 documents would need to be processed fiulher. And this document set, in fact, contained 92% of the judged-relevant documents.

The advantage of the cut-off criterion is it's sensitivity to the varied distributions of SFC similarity values for individual Topic Statements, which appears to reflect how "appropriate" a Topic Statement is for a particular database. For many queries, a relatively small portion of the database, when ranked by similarity to the Topic

Statement, will need to be further processed. For example, for Topic Statement forty-two, when the goal is 100%

recall, the regression formula predicts a cut-off criterion similarity value which requires that only 13% of the ranked

ou^ut be further processed, and the available relevance judgments show that this pool of documents contains 99% of

the documents judged relevant for that query.

2.C. V-8 Matching

Given the complete modularity of die first four modules in the system, for the twenty-four month TIPSTER testing, we reordered two modules so that Text Structuring is done prior to Subject Field Coding. This allowed us to implement and test a new version of matching which combines in a unique way the Text Structurer and the Subject

Field Coder. We refer to this version as the V-8 model, since eight SFC vectors are produced for each docmnent, one

for each of the seven meta-categories, plus one for all of the categories combined. The V-8 model, therefore, provides

multiple SFC vectors for each document, thereby representing the distribution of SFCs over the various meta-text COTuponents that occur in a news-text document. This means, in die V-8 matching, that if certain content areas of the Topic Statement are required to occur in a document in one meta-text compcment, e.g. CONSEQUENCE, and otiier content is required to occur in anotiier meta-text component, e.g. FUTURE, this proportional division can be

matched against the V-8 vectors produced for each dociunent at a fairly abstract, subject level. For the TIPSTER

twenty-four month evaluation, we have experimented with several formulas for combining the similarity values of

88
the multiple SFC vectors produced for each document, including both a Dempster-Shafer combination and a straight

averaging. Although official results are not yet available, our intemal test results indicate that the ccanbination of

Text Structuring and Subject Field Coding produces an improved ranking of documents, especially when using the Dempster-Shafer method.

2.D. Proper Noun Interpreter

Our earlier work with the SFCoder, suggested that the most important factor in improving the performance of this upstream ranking module, would be to integrate the general subject-level representation provided by SFCodes with a

level of text representation that enabled more refined discrimination. Analysis of earUer test results suggested that proper noun (PN) matching that incorporated both particular proper nouns (e.g. Argentina, FAA) as well as

'category' level proper nouns (e.g. third-world country, government agency) would improve precision performance.

The Proper Noun Interpreter (Paik et al, 1993) that we developed provides: a canonical representation of each proper noun; a classification of each proper noun into one of tiiirty-seven categories, and; a means for expanding group

noims into their constituent members (e.g. all the coxmtries comprising the Third World). Recent work on our proper noun algorithms, context-based rules, and knowledge bases, has improved the module's ability to recognize and

categorize proper nouns to 93% correct categorization using 37 categories as tested on a sample set of 545 proper nouns frcHn newspaper text The improved performance has a double impact on the system's retrieval performance, as proper noms contribute both to the downstream relation-concept representation used in CG matching as well as to the upstream proper noun, complex nominal, and text structure ranking of documents in relation to individual

queries. Details of processing Topic Statements for thek PN requirements and the use of this similarity value in document ranking is described in the later section on the Query Constructor.

2. E. Complex N""^'"al Phraser

A new level of natural language processing has been incorporated in the DR-LINK System with the implementation

of the Complex Nominal ((ZN) Phraser. The motivation behind this addition was our recognition that either, in addition to proper nouns, or in the absence of proper nouns, most of the substantive content requirements of Topic

Statements are expressed in complex nominals (i. e. noun + noun, e.g. "debt reduction", "government assistance", "health hazards"). Complex nominals provide a linguistic means for precise conceptual matching, as do proper

nouns. However, the conceptual content of complex nominals can be expressed in synonymous phrases, in a

different way than can the conceptual content of proper nouns, which are more particularized. Therefore, for complex

nominals, a controlled expansion step was incorporated in the CN matching process in order to accompUsh the desired goals of improved recall, as well as improved precision.

For input to the CN Phraser, the complex nominals in Topic Statements are recognizable as adjacent noun pairs or non-predicating adjective + noun pairs m the output of die part-of-speech tagger. Having recognized all CNs, the

substitutable phrases for each complex nominal are found by computationally determining the overlap of

synonymous terms suggested by RTF and statistical corpus analysis. These processes serve to identify all second

order associations between each complex nominal constituent and terms in the database. Second order associations

exist between terms that are used interchangeably in certain contexts. The premise here is tiiat if, for example, terms a and b are both frequentiy premodified by the same set of terms in a corpus, it is highly likely that terms a and b are

substitutable for each other within these phrases. The use of both corpus and RTT mformation appears to limit the over-generation that frequentiy results fi-om automatic term expansion. Ongoing experiments on this new addition to the system will help us further refine the process and wUl be reported more extensively in the near future.

The terms that exhibit second order associations are compiled into equivalence classes. These equivalence classes provide substitutable synonymous phrases for Topic Statement complex nominals and are used by the matching

algorithms in the same manner that the original complex nominals are used. The complex nominals and thek

substitutes are first used in the upstream matching of Topic Statements to docxmients as one contributing factor to the mtegrated similarity value, to be further explained in the section on the Query Constructor.

In addition, each complex nominal and its assigned relation provides a CRC to the RCD module for use in the final round of matching. For that module, semantic relations between the constituent nouns of each complex nominal are

89
assigned manually, using an ontology of forty-three relations. Some example complex nominals plus relation are:

[press] <- (SOURCE) <- [commentaries]

[growth] -> (MEASURE) -> [rate] [electronic] <- (MEANS) <- [theft] [campaign] <- (USED_FOR) <- [finances]

The development of the complex nominal C!RC knowledge base was an intellectual effort for the twenty four month

testing, but our current task is the full automation of the semantic relation assignment. Although difficult, our

experience with the intellectual process has encouraged us to pursue appropriate NLP-based machine-learning

techniques which will enable the system to automatically recognize and code semantic relations in complex

nominals.

In CG matching, the existence of both case-frame relations and complex nominal relations make it possible for the system to detect conceptual similarity even if expressed in different grammatical structures, such as a verb +

arguments in a Topic Statement and a complex nominal in a document, e.g.:

"reduce the debt" = [reduc*] -> (OBJECT) -> [debt] "debt reduction" = [debt] <- (OBJECT) <- [reduc*]

To achieve the fullest exploitation of relational information despite grammatical realization, a further step was

necessary in order to match on CRCs produced by verb-based analysis and CRCs produced by complex nominal

analysis. This required the determination of the degree of relation-similarity across the two relation sets. There are approximately sixty relations used in case frames, while there are approximately forty relations used in complex

nominals. A relation-similarity table was constructed that assigns a degree of similarity between twenty-eight pairs across the two grammatically-distinguished sets, and a degree of similarity between pairs within the same set. The

relation-similarity table is used in the final CG matching to allow concepts that are linked by a relation in a document that is different from the relation that links die same two concepts in the Topic Statement, to still be

awarded some degree of similarity. The quality and appropriateness of the similarity table will be determined by the

results of the twenty-four month testing which will also provide empirical evidence of the Complex Nominal

Phraser's impact on performance. Sample runs have indicated that the inclusion of complex nominals has a strongly

positive impact on oiu" results in both of its incorporations in the system.

2. F. Natural Language Query Constructor We have implemented a Natural Language Query Constructor (QC) for DR-LE^IK which takes as input a Topic

Statement which has been pre-processed by straight-forward techniques, such as part-of-speech tagging as well as SGML-tagging of the meta-language which reflects the typical request-presentation language used in Topic

Statements (e.g. "A relevant document will ..." or "To be relevant..."). The QC produces a query which reflects the appropriate logical combinations of the text structure, proper noun, and complex nominal requirements of a Topic

Statement. The basis of the QC is a sublanguage grammar which is a generaUzation over the regularities exhibited in the Topic, Description, and Narrative fields of die one hundred fifty TIPSTER Topic Statements. It should be noted

that the sublanguage grammar, with minor modifications, is capable of handling non-TIPSTER queries, so its generalized utiUty is promising. Earlier work (Liddy et al, 1991) demonstrated that the sublanguage approach is an

effective and efficient approach to natural language processing tasks witiiin a particular text-type, here Topic

Statements.

For the twenty-four month runs, the QC sublanguage grammar detects die required logical combination of text structure components, proper nouns, and complex nominals. These are the specific entities which we consider to be

particularly revealing indicators of relevant documents. In most cases, matching on diese classes produces highprecision ranked results, although there are some instances in which single common nouns may also be needed. After analyzing the twenty-four month results, we will determine whether to expand the range of linguistic types which

can be used to instantiate the variables in the QCs logical assertions.

90
The QC sublanguage grammar relies on function words (e.g. conjunctions, prepositions, relative pronouns), metalevel phrases (e.g. "stich as", "examples of, "as well as"), and punctuation (e.g. commas, semi-colons) to recognize

and extract the relevancy requirements of Topic Statements. These linguistic features serve as clues to the

'organizing' structure of a Topic Statement and present each Topic Statement's unique thematic content in a recognizable frame. The QC sublanguage mterprets a Topic Statement into pattem-action rules which are used to reduce each sentence in a Topic Statement into a first order logic assertion, reflecting the boolean-like requirements

of Tq)ic Statements, including NOT'd assertions. In addition, definite noun phrase anaphors are recognized and

resolved by sublanguage grammar processing rules.

2.G. Integrated Matcher

Each logical assertion produced by the QC for a Topic Statement is evaluated against the entries in the document

inverted file and a weight is assigned to each segment of text (either a clause or a sentence) which has any similarity.

The weighting scheme we are currently using evolved from iterative testing. Each segment of text is indexed in the inverted file with a text structure component label and will be assigned a weight if it contains any proper nouns or complex nominals that match the Topic Statement's requirements. The following weights are assigned:

proper noun = 1.00

complex nominal = 1.00 proper noun category = 0.50

This means, for example, that if, in response to die following requirement from a Topic Statement:

A relevant document will provide data on Japanese laws, regulations, andJor practices

which help the foreigner understand how Japan controls, or does not control, stock- market practices which could be labeled as insider trading.

a document text-segment contains 'Japanese law', and 'stock-market practice' (or one of its synonymous phrases), and

'insider trading' (or one of its synonymous phrases), that segment is assigned a preliminary value of 3.00. Depending on which field in the Topic Statement the assertion came from, and whether die document text-segment matches die Topic Statement's Text Structure requirement, the preliminary value will be multipUed by one of the following coefficients:

Topic field and required Text Structure component = 1 .(X) Desc, Narr, or Concept field and required Text Structure component = 0.75

Topic field and non-required Text Structure component = 0.50

Desc, Narr, or Concept field and non-required Text Shucture component = 0.25

So if 'Japanese law' and 'stock-market practice' and 'insider trading' were conceptual requirements from a Topic field assertion that also required them to occur in an EVALUATION or LEAD-MAIN text component, and they occiured

in a document text segment which has been tagged by the Text Stiiicturer as EVALUATION, the value of 3 would

be multiplied by 1; whereas if that assertion came from the Description field in die Topic Statement and die three required phrases occurred in a document text segment labelled CONSEQUENCE by the Text Structurer, the value of

3 would be multiplied by .25.

Since the QC interprets each sentence in the Topic. Description. Narrative, and Concept fields m a Tq)ic Statement,

multiple, sometimes overlapping, sometimes repetitive assertions are produced for a single Topic Statement. In tiie current implementation, each of these Topic Statement assertions is compared to die inverted document file, and die highest similarity value for a single assertion in the document is used as that document's integrated similarity value

for that Topic Statement.

The similarity value which results from the QC module matching is combined with the SFC similarity value of the document, and an integrated similarity score for each document is produced. This similarity value can be used in several ways. Firstly, the two similarity values can be used to provide a full ranking of all die docmnents which

91
takes into account the lexical, semantic and discourse sources of linguistic information in both docmnents and

queries. Secondly, it can serve as input to a filter which uses a more complex version of the original cut-off criterion to determine how many documents should be further processed by the system's final modules.

For the Integrated Matcher to produce a combined ranking, each document's similarity value for a given Topic

Statement can be thought of as being composed of two elements. One element is the SFC similarity value and one

element is the similarity value that represents the combined proper noun, complex nominal, and text structure

similarities. Additionally, the system will have computed the regression formula, die mean, and standard deviation of

the distribution of the SFC similarity values for the individual Topic Statement. Using these statistical values, the system produces the cut-off criterion value. Since we know from the eighteen-month results, that 74% of the relevant documents had what we refer to as a k-value (then FN value; now FN, CN, TS values) and the remaining

26% of the relevant documents had no k-value, we use this information to predict what proportion of the predicted

relevant documents should come from which segment of the ranked documents for fiiU recall. The combined ranking can be envisioned as consisting of four segments, as shown in Figure 2.

Docs, having a k-value

& an SFC value 1 Group 1 above the cut-off

cut-off criterion SFC similarity value

Docs, having a k-value

& an SFC value 1 Group 2

below the cut-off

Docs, having no k-value

& an SFC value 1 Group 3 above the cut-off

cut-off criterion SFC similarity value

Docs, having no k-value

& an SFC value 1 Group 4

below the cut-off

Fig. 2: Schematic of Segmented Ranks from SFC & Integrated Ranking (k-value)

Four groups are required to reflect the two-way distinction mentioned above. The first distinction is between those groups which have a k-value and which should contain 74% of the relevant documents and those documents without

a k-value, which should contribute 26% of the relevant documents. The second distinction is between those documents whose SFC similarity value is above the predicted cut-off criterion and those whose SFC similarity value

is not.

When a cut-off criterion is the application desired, the system will produce the ranked list in response to a desired

recall level, by concatenating die documents above the appropriate cut-off for that level of recall from (Jroup 1; then

docmnents above the appropriate cut-off for that level of recaU from Group 3. However, since our test results show

that there is a potential 8% error m die predicted cut-off criterion for 100% recall, we use extrapolation to add the appropriate proportion of the top ranked documents from Group 2 to Group 1, before concatenating documents from

Group 3. These same values are used to produce die best end-to-end ranking of all the documents using the various

segments.

Document ranks are produced by die Integrated Matcher and the cut-off criterion is used either by an individual user who requires a certain recall level for a particular information need, or. as in the twenty four month TIPSTER test situation, by die system to determine how many documents from die Integrated Matcher ranking will be passed on to the final modules for further processing.

92
2. H. Topic Statement Processing for Conceptual Graph Generation

The processing of topic statements for CG generation does not make use of the output of the Natural Language

Query Constructor, but instead the current system first applies the same RCD and CG generator modules to produce

topic statement (TS) CGs. Several TS-specrfic processing requirements have been identified, some of which have

been implemented as post-processing routines and others are under development.

- Elimination of concept and relation nodes corresponding to contentless meta-phrases (e.g. "Relevant

document must identify ..."). If both of the concept nodes in a concept-relation-concept triple belong

to a meta-phrase, the CRC is ignored. When only one of them is a meta-phrase concept, the triple is not removed blindly unless the other concept occurs m another triple.

- Handling of negated parts of topic statements. The weights are adjusted m such a way that an occurrence of the negated concept in a document will contribute to the negative evidence that the document will be relevant. In

effect, the two weights for the concept are switched.

- Automatic assignment of weights to concept and relation nodes. There are several factors we consider: the conventional way of determining the importance of terms using inverse document frequency (IDF) and total frequency; the location of terms occurring in topic statements; the part of speech information for each term; and

indications m the topic statement sublanguage (e.g. the document MUST contain ...). Although we have

implemented a program that tags individual words with the degree of importance based on the sublanguage

patterns, we assigned concept weights based on IDF values of terms in the collection for the evaluation, due to time constraints.

- Merging common concept appearing in different sections of topic statements. Although it is not safe in general to assume that two concepts sharing the same concept name actually refer to the same concept

instantiation and merge them blindly, we have observed that this is not the case in the topic statements. In fact, we believe that it is desirable to merge CG fragments using common concept nodes. This is an important process

that eliminates undesirable effects on scoring. Without this, a document containing a concept occurring repeatedly

in <desc>, <narr>, and <con> fields would be ranked unnecessarily high (or low if it is negated) because each

occurrence of the concept would make an independent contribution to the overall score.

Since an integrated automatic topic processing module was not available, the mechanical aspects of the process were

hand-simulated with some parts done automatically and other done manually.

2. 1. Relation Concept Detector (RCD)

The ouQ)ut of the Complex Nominal Phraser and the Proper Noun Interpreter modules described above provide

concept-relation-concept triples directly to the Relation-Concept Detector (RCD) module. In addition, the following

RCD handlers are operative.

One of the more distinct aspects of the DR-LINK system is its capability of extracting and using relations in the

final representation of documents and topic statements in thek CG representations. This module provides building

blocks for the CG representation by generating concept-relation-concept triples based on the domain-independent

knowledge bases we have been constructing with machine-readable resources and corpus statistics. In this module,

there are several handlers that are activated selectively depending on the input sentence.

2. 1. 1. Case Frame (CF) Handler

The main function of the CF Handler is to generate concept-relation-concept triples where one of the concepts comes

typically from a verb. It identifies a verb in a sentence and connects it to other constituents surrounding the verb. Since the relaticms (about 50 we use currently) included in our representation are originated from the theories of

linguistic case roles (Somers, 1987, and Cook, 1989) and are all semantic in nature, this module consults the

93
knowledge base contaming 13,786 case frames we have constructed, each of which prescribes a pattern involving a verb and the corresponding concept-relation-concept triples.

Given a set of case frames for different senses of "decline", for example,

(decline 1 ((PATIENT subject ? obUgatory)))

(decline 2 ((AGENT subject himian obligatory)

(PATIENT object ? optional)))

(decline 3 ((AGENT subject human obligatory)

(ACnvnY infinitive ? obligatory)

(link infinitive subject AGENT)))

AGENT, PATIENT, and ACTIVITY are the relations that connect the verb to other constituents. The second

components (e.g. subject) prescribe the syntactic categories of the constituents and the third components (e.g. human) semantic restrictions that the subject and object should satisfy. The last components (e.g. obligatory)

indicate whedier the constituent must exist in a sentence in order for the particular case frame to be instantiated. The

last line of the diird case frame instructs the CF handler to link the subject to the infinitive verb with the AGENT

relation. This kind of linking instructions allow the CF handler to produce triples containing non-verbal

constituents.

The CF Handler selects the best case fi-ame by attempting to instantiate each case frame and determine which one is satisfied most by the sentence at hand. This can be seen as a sense disambiguation process using both syntactic and

semantic information. The semantic restriction ioformation contained in the case frames were obtained from

LDOCE, and when the sentence is processed, the CF handler also consults LDOCE to get semantic restriction information for individual constituents siuxounding the verb in die sentence and compares it with the restrictions in the case frames of the verb as a way to determine which case frame is likely to be the correct one.

With the following sentence fragment,

... the chairman declined to elaborate on the disclosure ...

the CF handler chooses the third case frame and produces

[decline] -> (AGENT) -> [chairman]

[decUne] -> (ACTIVITY) -> [elaborate]

[elaborate] -> (AGENT) -> [chairman]

In the current implementation, the input text to the CF handler is first tagged with part-of-speech information and

bracketed for constituent boundaries. BBN's POST tagger (Metter et al., 1991) has been used to attach a

part-of-speech tag to individual words. The constituent boundary bracketer we developed then marks boundaries of

grammatical constituents such as infinitives, noun phrases, prepositional phrases, clauses, etc.

At the time of writing, the case frame knowledge base contains 13, 786 case frames, of which 13,444 are for all the verb entries (5,206) in LDOCE, and the rest are for 342 verbs that appear in die Wall Street Journal collection 1)ut are not in the LDOCE as a headword. While we have constructed case frames for most of die phrasal verbs in LDOCE, the capabiUty of processing phrasal verbs has not been implemented in the current CF Handler.

2. 1. 2. Nominalized Verb (NV) Handler

The nominaUzed verb handler has been implemented for the DR-LINK system we ran for the TIPSTER 24th month

evaluation. Its main function is to consult die NV case frames to identify a NV in a sentence and create

94
concept-relation-concept triples based on the rule. At the same time, it converts the NV into its verb form. In this way, we can allow for a match between a CG fragments generated from a phrase containing verb and another fragment generated from a noim phrase containing the corresponding nominahzed verb. For example, the NV Handler

converts the sentence fragment

... the company's investigation of the incident ...

into

[investigate] -> (AGENT) -> [company]

[investigate] -> (PATENT) -> [incident].

This process is much more than a sophisticated way of performing stemming in that we canonicalize

concept-ielation-concept triples radier than just concept nodes.

For NV processing, 15, 053 case frames have been generated for 1,593 nominalized verbs. Most of the case frames

for NVs were automatically generated from the corresponding verb case frames. This process was also faciUtated by

identifying potential NVs from LDOCE.

No explicit testing of the impact of NVs in mformation retrieval has been done yet although we have convinced

ourselves with anecdotal evidence that this would improve the retrieval performance. More semantic processing of

nominalized verbs in determining the relations to the surroimding constituents is on the future research agenda. More

rigorous study on the impact of NVs on information retrieval should be done, too.

2. 1. 3. Noun Phrase (NP) and Prepositional Phrase (PP) Handler

The noun phrases that are not handled by the complex-nominal handler or by the nominalized verb handler are analyzed so that the head noun is connected to the concepts outside the noun phrase (e.g. a verb concept in the CF

Handler). In addition, this module identifies individual concepts corresponding to adjectives and other noims in a compound noun and connects them with CHARACTERISTIC, ATTRIBUTE, or LINK relations. LINK is the most

generic relation in our system.

Once noun phrases are handled this way, this module handles prepositional phrases by connecting the head noun

concept of the noun phrase to the preceding constituent (e.g. a verb or a noim). The preposition attachment problem

is a difficiilt one, and the current implementation takes the simple- minded approach with general relations such as LINK, which can match with many of odier semantically more specific relations. Our preliminary analysis indicates

that this approach correcdy handles about 75% of the prepositional phrase cases in the Wall Street Journal

collection. More accurate and finer-level processmg will be done with more semantically oriented rules diat check

the semantic restrictions and use more specific relations. The role of this handler will be diminished when we process

phrasal verbs as part of the CF handler, for which we have constructed case frames.

2.1.4. Ad-hoc Handler

This module looks for lexical patterns not covered by any of the other special handlers discussed above. Its processing is also driven by its own knowledge base of patterns to infer relations between concepts. For example, a

sentence fragment

... bought the item for the purpose of satisfying ...

contains a pattern

[VERB] ... for the (ADJ) purpose of [NP]

in the knowledge base, and hence results in a triple

95
[buy] -> (GOAL) -> [satisfy]

The knowledge base contains a small number of simple patterns involving BE verbs and more than 350 pattern rules for phrasal patterns across phrase boundaries, by which important relations are extracted. The pattern rules specify

certain lexical patterns and the order of occurrences of words belonging to certain part-of-speech categories, and the concept-ielation-concept triples to be generated. These patterns require a processing capability no more powerful than

a finite state automaton. Due to the time constraints, however, the current ad-hoc handler has not been generaUzed to process all the patterns, and about 30% of the patterns in the knowledge base are recognized and handled correcdy.

2. J. Conceptual Graph (CG) Generator

After individxial RCD modules have generated concept-relation-concept triples for a document, the CG generator

merges them to form a set of conceptual graphs, each corresponding to a clause in most cases. Since more than one

handler can generate different triples for the same concept pairs (e.g. a prepositional phrase handled by the C!F handler

and the NP/PP handler) based on independently constructed rules and on independent processes, a form of conflict resolution is necessary. In the current implementation, we simply order the execution of different handlers based on

the general quality of the rules and the resulting triples so tiiat more reliable handlers have higher precedence.

The concept nodes in die resulting CGs can not only contain general concept names but also some instantiations

(referents) of the concepts. Such a concept can be derived either from a proper noun such as a company name or from

a sub-ordiaate clause. In the latter case, the instantiation is a CG itself to produce a CG like

[country: {US}] <- (SOURCE-OF-INFO) <- [C#: [[pact] <- (PATIENT) <- ... ]

In the current implementation, concepts with the same instantiation are merged across sentences to form a larger CG,

but concept with the same label but without any referents across sentences are treated as separate concepts and are not merged. A pronoun resolution method is being implemented to merge a pronoxm to its antecedent as a way to increase the connectivity of CGs and hence increase the usefulness of relation nodes.

As a way to make our current representation more "conceptual", we have implemented a module that adds RTF

(Roget's International Thesaurus) codes to individual concept nodes so that the label on the nodes is not a word but a

position of the hierarchy of RIT. The lowest level position beyond individual lexical items in the RTT hierarchy is called a semi-colon group consisting of several terms within tbe deUmiter of semi-colons, which represents a concept.

The mapping from a word (called target) in text to a position in RTT requires sense disambiguation, and our approach

is to use the words surrounding the target word as the context within which the sense of the target word is determined

and one or more RIT codes are selected. The algorithm selects minimal number (i.e. one or more) of RTT codes, not

just the best one, for target words since we feel that some of the sense distinctions made in RTT are unnecessarily

subtie, and it is imlikely diat any attempts to make such fine distinctions would be successful and hence contribute

to information retrieval. We have produced RJT-coded documents and topic statements for the San Jose Mercury collection and the routing

queries. All the concept nodes derived from nouns now have RTT codes selected using the surrounding text as the

context. Those concept nodes derived from verbs also have RTT codes but in a different way. Instead of using the surrounding text as the context and trying to disambiguate senses (we concluded that this method is not reliable for

verbs), we first assign RTT codes to each sense of LDOCE verb entries using the same method. In this case the context become the definition text in LDOCE. Once we select the right case frame by Case Frame Handler while text

is processed, the RTT codes attached to the case frame are automatically assigned to the target verb.

2.K. Conceptual Graph (CG) Matcher

The main function of the CG matcher is to determine the relevance of each document against a topic statement CG

96
and produce a ranked list of documents as the third and final output of the system. Using the techniques necessary to model plausible inferences with CGs (Myaeng and Khoo, 1992), this module computes the degree to which the topic statement CG is covered by the CGs in the document (see Myaeng and Liddy (1993) and Myaeng & Lopez-lopez

(1992) for details).

While the most obvious strength of the CG approach is its ability to enhance precision by exploiting the structure of the CGs and the semantics of relations in document and topic statement CGs, and by attempting to meet the

specific semantic constraints of topic statements, we also attempt to increase recall by allowing flexibility in node-level matching. Concept labels can be matched partially (e.g. between "^Bill Clmton' and 'Qinton'), and both

relation and concept labels can be matched inexactly (e.g. between 'aid' and 'loan' or between 'AGENT' and

'EXPERIENCER'). For both inexact and partial matches, we determine the degree of matching and apply a multiplication factor less than 1 to the resulting score. For inexact matching cases, we have used a relation

similarity table that detennines the degree of similarity between pahs of relations. Aldiough diis type of matching

slows down the matching time, we feel that until we have a more accurate way of determining the conceptual

relations and a way to represent at a truly conceptual level (e.g. our attempt to use RTF codes), it is necessary. More

importanfly, the similarity table reflects om- ontology of relations and allows for matching between relations produced by different RCD handlers whose operations in turn are heavily dependent on the domain-independent

knowledge bases. We have done a series of matching experiments mtemally to evaluate various strategies in CG matching/scoring and

document representation with the goal of selecting the best one for the final TIPSTER 24th month runs. The first question we had was how to "normahze" the score assigned to a dociunent based on the current scoring scheme. As

described above, the scoring algorithm is query-oriented in the sense that the score reflects to what extent the query

CG is covered by the document CG. While this approach is theoretically justifiable, one potential drawback is that a document containing the entire query CG is not ranked higher than one that contains fi-agments of the query CG

scattered in the document as long as they cover the same query CG- That is "connectivity" or 'coherence" of

matching docmnent CG is not fully taken into accoimt.

With the intuitive notion that the number of matching CG fragments in a document would be inversely proportional

to "connectivity", we have been experimentmg with various normalization factors that are a function of die number

of matching CG fragments. At the time of writing, our experimental data show that when we consider 12 sentential CGs as a unit (called "paragraph") and use the number of units containing one or more matching CG fragments m

the normaUzation function, we obtam the best result. Among all the functions we have tried, the best normalization

factor we have found experimentally so far is:

l.QS'^d-x)

where x is the number of text units that contain one or more matching CG fragments. When this is combined with

the maximum of the scores assigned to individual "paragraph" as foUows:

S*1.05'^(1-x)-h0.4*M

where S is for the unnormalized score and Mfor the maximum "paragraph" score, we obtained the best results. Since we determined the constants incrementally, it is entirely possible that different combmation of die constants can give

better results. It is relatively clear based on these experiments that the first or die second term alone are always

inferior to the combination. The number of sentential CGs for "paragraphs". 12, seems also pretty stable. We have produced TIPSTER runs using the RTT-coded documents and topic statements. The current matching

program attempts to match on RIT codes only when the concept names (words) don't match. Because of this conservative approach, die RTT codes do not block a match between two different polysemous words and thus have

any direct impact on the word ambiguity problems in IR. With the disambiguation process employed when RTT

codes are chosen for a noun or verb, however, the net effect is analogues to term expansion with sense

disambiguation. It should be noted tiiat since RTT codes are used for bodi document and query concepts, diis amoimts

to sense-disambiguated term expansion on both queries and documents.

97
While the original motivation was to represent documents and topic statements at more conceptual level using RTT

codes, we are also testing the effectiveness of RTT-based term expansion in IR environments. Using the scheme we

have developed for term clustering using contextual information in the corpus (Myaeng & Li, 1992), we have three methods to evaluate: RTT-based expansion, term-cluster based expansion, and a combination of the two so Uiat we

can eliminate the problem of using a general- purpose thesaurus and the errors made by the term-clustering method.

For TIPSTER evaluation, we have submitted two sets of four runs: one with RTT codes and the other without them.

Each set consists of three runs for different scoring schemes and the last one for the combination of the three runs which appears to produce the best result in our intemal experiment.

3. Test Runs

The DR-LINK group elected to put their efforts into continued work for the twenty-four montii TIPSTER testing,

and as a result we lost our opportunity to have TREC-compatible results to discuss at this time. Although our

twenty-four month TIPSTER runs have been submitted, many of our top-ranked documents were not amongst those submitted by TREC participants, so it is virtually impossible to make even unofficial reports on our system's

performance. We trust that in the near future there will be some comparable groups and/or runs to measure ourselves

against after the results from both TIPSTER and TREC-2 are available.

As the above descriptions should convey, we have made a great deal of progress in the development and integration

of the DR-LINK System since TREC-1. Unfortunately, the absence of quantified results of our performance limits our convincing power. However, we are pleased to have demonstrated that a system implementation of our original

notion of integrating multiple levels of linguistic processing so tiiat retrieval can be conducted at a conceptual rather than word-based level is nearly achieved.

Many rich research and implementation ideas remain to be explored in all of the DR-LINK modules, particularly

those which have only been in existence for a few months.

5. Acknowledgments

The research reported herein was funded by ARPA's TIPSTER Program. We are grateful to botii Longmans' for access to the machine readable tape of LDOCE and to BBN for the loan of their POST tagger.

Without the untiring efforts of the following individuals, neither the DR-LINK System nor our very promising

results would have been possible: Margot Clark, Bob Del Zoppo, Saleh Elmohammed, Chris Khoo, Tracey Lemon.

Ming Li, Mary McKenna, Ken McVearry, Carin Obad, Woojin Paik, Ching-cheng Shih, Joe Woelfel, Edmund Yu,

Ahsan Zhia.

References

Cook, W. (1989). Cfl^? (rffimm ar Theory. Washington, D.C. : Georgetown University Press.

Harman, D. (Ed.) (1993). Th'-, first Text REtrieval Conference (TREC-l). National histitute of Standards and

Technology.

Liddy, E.D., Jorgensen, C.L., Sibert, E. & Yu, E.S. (1991). Sublanguage grammar in natural language

processing. PrnrftftHings of RIAO '91 Conference. Barcelona.

Liddy, E.D., Paik, W. & Yu, E.S. (1993). Document filtering using semantic information from a machine readable

dictionary. Prnref/iings of the ACL Workshop on Very Large Corpora.

98
Meteer, M., Schwartz, R. & Weischedel, R. (1991). POST: Using probabilities in language processing.

P^OCgPrf^'^^ gs of the Twelfth International Conference on Artificial Intelligence. Sydney, Australia.

Myaeng, S. H. & Khoo, Chris (1992). On uncertainty handling in plausible reasoning with conceptual graphs.

Proceedmgs of the 7th Conceptual Graphs Workshop. Las Cruces, NM.

Myaeng, S. H. & Li, Ming (1992). Building a database-specific concept hierarchy for information retrieval by

acquiring lexical semantics from a corpus. Prrx'j'if.iijin gs of the First International Conference in Tnf^^rm^^ti^n an(j

Knowledge Management. Baltimore, MD.

Myaeng, S. H. & Liddy, E. D. (1993). Information retrieval with semantic representation of texts. PrnrpfHings nf

.Symposium on Document Ana1y<fi:if ^nf) Ttifnrm{>H(;^n Retrieval. Las Vegas.

Myaeng, S. H. & Lopez-Lopez, A. (1992). Conceptual graph matching: A flexible algorithm and experiments.

Journal of Experimental and Theoretical Artificial Intelligence. Vol. 4, 107-126.

Paik, W., Liddy, E.D., Yu, E.S., & McKenna, M. (1993). Categorizmg and standardizing proper nouns for efficient information retrieval. Proceedings of the Workshop on Acquisition of Lexical Knowledge from Text. Association

for Computational Linguistics, pp. 154-60.

Somers, H. L. (1987). Valency and Case in Computational Linguistics. Edinburgh: Edinburgh University

Press.

van Dijk, T. A. (1988). News as discourse. HillsdaleLawrence Erlbaum Associates.

99

Feedback and Mixing Experiments with Match Plus

Stephen 1. Gallant' William R. Caid^ Joel Carleton^ Todd W. Gutschow^

Robert Hecht-Nielsen^ Kent Pu Qing^ David Sudbeck^

HNC Inc

Abstract

We briefly review the MatchPlus system and describe

recent developments with learning word representations, experiments with relevance feedback using neural network learning algorithms, and methods for combining diff"erent output lists.

1 Introduction

HNC is developing a neural network related approach

to document retrieval called MatchPlus^ . Goals of

this approach include high precision/recall performance, ease of use, incorporation of machine learning

algorithms, and sensHiviiy io similaniy of use. To understand our notion of sensitivity to similarity of use, consider the four words: 'car', 'automobile',

'driving', and 'hippopotamus'. 'Car' and 'automobile' are synonyms and they very often occur together

in documents; 'car' and 'driving' are related words

(but not synonyms) that sometimes occur together

in documents; and 'car' and 'hippopotamus' are es- sentially unrelated words that seldom occur within

the same document. We want the system to be sensitive to such similarity of use, m^uch like a built-in thesaurus, yet without the drawbacks of a thesaurus,

such as domain dependence or the need for handentry of synonyms. In particular we want a query on

'car' to prefer a document containing 'drive' to one

containing 'hippopotamus', and we want the system

itself to be able to figure this out from the corpus. The implementation of MatchPlus is motivated by

neural networks, and designed to interface with neural network learning algorithms. High-dimensional

(w 300) vectors, called context vectors, represent

word stems, documents, and queries in the same vector space. This representation permits one type of

•124 Mt Auburn St, Suite 200. Cambridge, MA 02138

'5501 Oberlin Drive, San Diego. CA 92121. ^Patents pending.

neural network learning algorithm to generate stem

context vectors that are sensitive to similarity of use, and a more standard neural network algorithm to per- form routing and automatic query modification based

upon user feedback, as described below.

Queries can take the form of terms, full documents,

parts of documents, and/or conventional Boolean ex- pressions. Optional weights may also be included.

The following sections give a brief overview of our implementation, and look at some recent improvements and experiments. For a previous description of

the approach and comments on complexity considerations see [1]; a longer journal article is in preparation.

2 The Context Vector Approach

One of the most important aspects of MatchPlus is

its representation of words (stems), documents, and

queries by high (« 300) dimensional vectors called context vectors. By representing all objects in the same high dimensional space we can easily:

1. Form a document context vector as the (weighted) vector sum of the context vectors for those words (stems) contained in the document.

2. Form a query context vector as the (weighted)

vector sum of the context vectors for those words

(stems) contained in the query.

3. Compute the distance of a query Q to any document. Moreover if document context vectors are normalized, the closest document d (in Euclidean distance) has the context vector that

gives highest dot product with the query context

vector V:

<closest d> = {d\V'^-V'^ is maximized for d e D]

(proof: yv'-V^^lp = ||V'^||- + ||V«||--2(V^-V«) =

const - 2(V'^ V'?).)

101
4. Find the closest document to a given document

d by treating as a query vector.

5. Perform relevance feedback. If d is a relevant

document for query Q, form a new query vector

where a is some suitable positive number (eg 3). (See also [8].) Note that search with takes

the same amount of time as search with V*^.

2.1 Context Vector Representations

Context vector representations (or feature space rep- resentations) have a long history in cognitive science. Work by Waltz &: Pollack [10] had an especially strong

influence on the work reported here. They described a

neural network model for word sense disambiguation

and developed context vector representations (which

they termed micro-feature representations). See Gallant [2] for more background on context vector repre- sentations and word sense disambiguation.

We use context vector representations for document retrieval, with all of the representation being

learned from an unlabeled corpus. A main constraint

for all of this work is to keep computation and storage

reasonable, even for very large corpora.

2.2 Bootstrap Learning

Bootstrapping is a machine learning technique that

begins with vectors having randomly generated positive and negative components, and then uses an

unlabeled training corpus to modify the vectors so that similarly used terms have similar representations. Previously we had used partially hand-entered

components as described in [2], but we have dispensed

with all hand entry in current im.plementations.

Although there are important proprietary details,

the basic idea for bootstrapping is to make a stem's

vector more like its neighbors by adding a fraction

of their vectors to the stem in question. We make

use of a key property of high-dimensional vectors: the ability to be 'similar to' a multitude of vectors. This is the same property that allows the vector sum

that represents a document to be similar to individual

teim vector summands. (Similarity between normalized vectors is measured by their inner product.)

Note that bootstrapping takes into account local word positioning when assigning the context vector representation for stems. Moreover it is nearly in- variant with respect to document divisions within the

training corpus. This contrasts with those methods

where stem representations are determined solely by

those documents in which the stem lies.

2.3 Context Vectors for Documents

Once we have generated context vectors for stems it is easy to compute the context vector for a document.

We simply take a weighted sum of context vectors

for all stems appearing in the document^ and then

normalize the sum. This procedure applies to documents in the training corpus as well as to new documents. When adding up stem context vectors, we can

use term frequency weights similar to conventional IR

systems.

2.4 Context Vectors for Queries; Relevance Feedback

Query context vectors are formed similarly to document context vectors. For each stem in the query we

can apply a user-specified weight (default 1.0). Then

we can sum the corresponding context vectors and

normalize the result. Note that it is easy to implement traditional rel- evance feedback. The user can specify documents

(with weights) and the document context vectors are merely added in with the context vectors from the

other terms. We can also find documents close to a

given document by using the document context vector as a query context vector.

2.5 Retrieval

The basic retrieval operation is simple; we find the

document context vector closest to the query context

vector and return it. There are several important

points to note.

1. As many documents as desired may be retrieved,

and the distances from the query context vector

give some measure of retrieval quality.

2. Because document context vectors are normalized, we may simply find the document d that

maximized the dot product with the query context vector, V:

maxiV^ • V^}.

d

3. It is easy to combine keyword match with context vectors. We first use the match as a filter for documents and return documents in order by

closeness to the query vectors. If all matching

^Stopwords are discarded.

102
documents have been retrieved, MatchPlus can

revert to context vectors for finding the closest remaining document.

4. MatchPlus requires only about 300 multiplications and additions to search a document. Moreover it is easy to decompose the search for a cor- pus of documents with either parallel hardware

or, less expensively, several networked conventional machines (or chips). Each machine can

search a subset of the document context vectors and return the closest distances and document

numbers in its subset. The closest from among

the distances returned by all the processors then

determines the documents chosen for retrieval. We also plan to investigate a cluster tree pruning procedure that finds nearest neighbor document context vectors without having to compute

dot products for all document context vectors. This data organization affects retrieval speed,

but does not change the order in which documents are retrieved.

3 Experiments and Runs Submitted

The four runs (two ad-hoc, two routing) submitted

to TREC-II are described in the following sections.

3.1 Totally Automated

This run used the entire topic as a query, with weight

of 2 applied to the topic section. We also employed

a SMatch filter that put documents having at least 4 of the concept terms before other documents. Context vectors determined the actual order of the documents (as well as which documents were in the 1000-

document submission list.) Note that these ad hoc

retrieval runs were totally automated from topic to

retrieval list.

3.2 Relevance Feedback From the

First 20 Retrievals

Here we took the top 20 documents from the previous

section, read them to estimate relevance, and then

modified the initial query for the remaining 980 re- trievals. To change a query we added and normalized

all relevant document context vectors from the first 20 retrievals and then added this vector to 0.7 times

the original query vector. (The 0.7 factor was obtained from experiments with a difierent corpus.)

3.3 Routing Using Neural Network

Learning and Output Mixing

Both routing runs used two types of neural network

learning.

3.3.1 Stem Weight Learning

The Stem Weight Learning approach uses neural net- work learning to compute weights for terms in a

query; there is one neural network input for every query term.^

Every judged document for a query provides a

training example as follows. Let V" be the context

vector for judged document n. If query term i has

context vector V, then the i^^ network input for training example n is given by V V" , the inner product of V* and V". For training example n, the desired

network output is ±1, according to the relevance of

document n. A fast single-cell learning algorithm, the pocket al- gorithm with ratchet [3, 4], generated weights. (More

complex algorithms did not produce better results.)

These weights were then used as term weights for nor- mal query processing.

Note that Wong, Yao, et al [9] previously used a

similar approach, applying a variant of perceptron

learning to learn weights with the SMART vector space model.

3.3.2 Full Context Vector Learning

This approach is similar to the previous Stem Weight

approach, except we compute an entire query context vector rather than weights for stems. Here we

use document context vectors directly as inputs to the network; for example the i network input for training example n is given by V"j. The network

weights produced by learning are directly interpreted

as a query context vector. For most topics, this approach was not as good as the previous approach for two apparent reasons. First the Stem Weight approach makes use of the original

query terms, a valuable piece of user input. Second,

the Stem Weight approach uses fewer (5-50) trainable

parameters, possibly avoiding a tendency with the

Full Context Vector approach (w 300 parameters) to

'overfit the data'.

3.3.3 Routing Run #1: Best Candidate

Our first routing run was to take the best candidate

from 4 sources: the two neural network approaches,

'Only terms in the concept section of the topic were used

for routing experiments.

103
a fully automated query run (as with the first ad hoc

submission), and a top 20 feedback run (as with the

second ad hoc submission). For each topic, the best

method was estimated from retrievals on a separate

test corpus (not the corpus used for submissions).

The winning method's list of 1000 retrievals was then

selected as the retrievals for this topic.

3.3.4 Routing Run #2: Mix and Match

Our second routing run was to mix retrievals from the same 4 sources, using a 'quality estimate' consisting

of 11-point recall/precision scores determined from a

run on a separate test corpus. Each document in each

of the four approaches was given points proportional

to the run quality estimate and inversely proportional

to its position number on an output list. Documents

appearing on more than one list received points for each appearance.

Mix and Match worked better than the previous

Best Candidate approach.

4 Comments

We are generally pleased with the performance from

our one-year-old system's results. (For final figures, see the appendix of this proceedings.)

In examining the data, one interesting aspect is that MaichPlus does better when measured by 11- point averages than by number of relevant documents

retrieved. This means a comparatively higher per- centage of documents in early retrievals were judged

relevant.'* We are now running initial experiments with word

sense disambiguation using context vectors and clus- tering. It will be interesting to see whether word sense disambiguation can further improve retrieval perfor- mance.

References

[l] Gallant, S. I. Context Vector Representations for Document Retrieval. AAAI-91 Natural Language Text Retrieval Workshop, Anaheim, CA, July 15, 1991.

[2] Gallant, S. I. A Practical Approach for Representing Context And for Performing

Word Sense Disambiguation Using Neural

^Tliis also raises the question as to whether comparatively

fewer total documents were scored by the readers, perhaps due

to the MaichPlus system's different approach. Computing the median number of documents scored per topic would resolve

this question, as well as providing an indication of "novelty"

for TREC participants.

Networks. Neural Computation, Vol. 3, No.

3, 1991, 293-309.

[3] Gallant, S. I. Perceptron-Based Learning Al- gorithms. IEEE Transactions on Neural Networks, Volume 1, Number 2.

[4] Gallant, S. I. Neural Network Learning and

Expert Systems. MIT Press, 1993.

[5] Hinton, G. E. Distributed Representations.

Technical Report CMU-CS-84-157, CarnegieMellon University, Department of Computer

Science. Revised version in Rumelhart, D.

E. & McClelland, J. L. (Eds.) Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1. MIT Press, 1986.

[6] Salton, G. The SMART retrieval system - Ex- periments in automatic automatic document

processing. Englewood Cliffs, NJ: PrenticeHall, 1971.

[7] Salton, G. & Buckley, C. Term-Weighting Approaches in Automatic Text Retrieval. Infor- mation Processing & Management, Vol. 24, No. 5, 1988, pp. 513-523.

[8] Salton, G. Buckley, C. Improving Retrieval Performance by Relevance Feedback. Journal

of the American Society for Information Sci- ence, 41(4):288-297, 1990.

[9] Wong, S.K.M, Yao, Y.Y, Salton, G. & Buckley, C. Evaluation of an Adaptive Linear

Model. Journal of the American Society for Information Science, 42(10):723-730, 1991.

[10] Waltz, D. L. & Pollack, J. B. Massively Parallel Parsing: A Strongly Interactive Model

of Natural Language Interpretation. Cognitive Science 9, 51-74 (1985).

104
Latent Semantic Indexing (LSI) and TREC-2

Susan T. Dumais

Bellcore

445 South St.

Morristown, NJ 07960

std(S)belIcore.com

1. Overview of Latent Semantic Indexing

Latent Semantic Indexing (LSI) is an extension of the vector retrieval method (e.g., Salton & McGill, 1983)

in which the dependencies between terms are

explicitly taken into account in the representation and

exploited in retrieval. This is done by sunultaneonsly

modeling all the interrelationships among terms and

documents. We assume that there is some underlying

or "latent" structure in the pattern of word usage

across documents, and use statistical techniques to estimate this latent structure. A description of terms,

documents and user queries based on the underlying,

"latent semantic", structure (rather than surface level word choice) is used for representing and retrieving

information. One advantage of the LSI representation

is that a query can be very similar to a document even when they share no words.

Latent Semantic Indexing (LSI) uses singular-value

decomposition (SVD), a technique closely related to eigenvector decomposition and factor analysis

(Cullum and Willoughby, 1985), to model the

associative relationships. A large term-document

matrix is decomposed it into a set of /: , typically 100

to 300, orthogonal factors from which the original

matrix can be approximated by linear combination.

Instead of representing documents and queries directiy as sets of independent words, LSI represents them as continuous values on each of the k orthogonal

indexing dimensions. Since the niunber of factws or dimensions is much smaller than the number of unique

terms, words will not be independent. For example, if two terms are used in similar contexts (documents),

they will have simil ar vectors in the reduceddimension LSI representation. The SVD technique can capture such structure better than simple term- term or document-document correlations and clusters. LSI partially overcomes some of the deficiencies of

assuming independence of words, and provides a way

of dealing with synonymy automatically without the

need for a manually constructed thesaurus. LSI is a completely automatic method. (The Appendix

provides a brief overview of the mathematics

underlying the LSI/SVD method. Deerwester et al., 1990, and Furnas et al., 1988 present additional mathematical details and examples.)

One can also interpret the analysis performed by SVD

geometrically. The res;alt of the SVD is a vector

representing the location of each term and dociunent

in the ^ -dimensional LSI representation. The location

of term vectors reflects the correlations in their usage

across documents. In this space the cosiae or dot

product between vectors corresponds to their estimated similarity. Retrieval typically proceeds by

using the terms in a query to identify a point in the

space, and all documents are then ranked by their similarity to the query. However, since botii term and

document vectors are represented in the same space,

similarities between any combination of terms and

documents can be easily obtained.

The LSI method has been applied to many of the standard IR collections with favorable results. Using

tiie same tokenization and term weightings, the LSI

method has equaled or ou^rformed standard vector mediods and other variants m almost every case, and was as much as 30% better in some cases (Deerwester

et al., 1990). As with the standard vector method,

differential term weighting and relevance feedback

both improve LSI performance substantially (Dumais,

1991). LSI has also been applied in experiments on

relevance feedback (Dumais and Schmitt. 1991), and

in filtering applications (Foltz and Dumais. 1992).

The recent MatchPlus system described by Gallant et

al. (1992) is related to LSI. Both systems model the

relationships between terms by looking at the

similarity of the contexts in which words are used, and

exploit these associations to improve retrieval. Both

systems use a reduced dimension vector

105
representation, but differ in how the term, document

and query vectors are formed.

2. LSIandTREC-1

We used the TREC-1 conference as an opportunity to "scale up" our tools, and to explore the LSI

dimension-reduction ideas using a very rich corpus of

word usage. We were pleased that we were able to use many of the existing LSI/SVD tools on the large

collection. (See Dumais, 1993 for details.) We were

able to compute the SVDs of 50k docs x 75k words

matrices without niunerical or convergence problems on a standard Dec5000 or Sparc 10 workstation.

Because of these limits on the size of the matrices we

could handle, we divided the TREC-1 documents into 9 separate subcollections (API, DOEl, FRl. WSJl,

ZIFFl, AP2, FR2, WSJ2, ZIFF2) and worked with

these. There are also some theoretical reasons why

working with subcollections makes sense. By using

topically coherent subcollections one can get better discrimination among documents. When the ZIFF

subcoUection is analyzed separately, for example, 2(X) or so dimensions can be devoted to differences among

computer-related topics. When these same documents

are part of a large corpus, many fewer mdexmg

dimensions will be devoted to discriminating among

them.

In terms of accuracy, LSI performance in TREC-1

was about average. There were some obvious

problems with our initial pre-processing of documents

(e.g., 'U.S." and "A.T.T." were omitted), and tiiere were some unanticipated problems in combining

across subcollections. Since many of the top

performing automatic systems used SMART'S

preprocessing, we chose to do so as well for TREC-2.

In addition, using the SMART software for this purpose allows us to compare LSI with the comparable vector method, so that we can examine

the contribution of LSI per se. We also planned on

comparing an LSI analysis using subcollections (from

TREC-1) with an LSI analysis of the entire collection for TREC-2.

We had high hopes of being able to build on our TREC-1 work for TREC-2. In practice, however, the changes in the pre-processing algorithm, and the decision to use a single combined LSI analysis resulted in our "starting from scratch" in many

respects. We have completed some experiments for TREC-2, but we did not get as far as we would have

liked, especially for the adhoc topics.

3. LSI and TREC-2

3.1 Pre-processing

We used the SMART system^ for pre-processing the documents and queries. Some markups (e.g. o

delimiters) were removed, and all hand-indexed

entries were removed from the WSJ and ZIFF

collections. Upper case characters were translated into lower case, pimctuation was removed, and white

spaces were used to deUmit terms. The SMART stop

list of 571 words was used as is. The SMART

stemmer (a modified Lovms algoridim) was used

without modification to strip words endings. We did

not use: phrases, proper noun identification, word

sense disambiguation, a diesaurus, syntactic or semantic parsing, spelling checking or correction,

complex tokenizers, a controlled vocabulary, or any

manual indexing.

The result of this pre-processing can be thought of as a term-document matrix, in which each cell entry

indicates the frequency with which a term appears in a document. The entries in the term-dociunent matrix were then transformed using an "Itc" weighting. The

"Itc" weighting takes the log of individual cell entries,

multiplies each entry for a term (row) by the IDF

weight of the term, and then normalizes the document

(col) length.

We began by processing the 742358 documents from

CD-I and CD-2. Using the minimal pre-processing

described above, there were 960765 unique tokens. 512251 imique stems, and 81901331 non-zero entries

in the term-document matrix. 742331 documents

contained at least one term. To decrease the matrix to a size we thought we could handle, we removed

tokens occurring in fewer than 5 documents. This

resulted in 781421 unique tokens, 104533 unique

stemmed words, and 81252681 non-zero entries. We

used the resulting 742331 document x 104533 term

matrix as the starting point for results reported in this paper. The "Itc" weights were computed on this matrix.

3.2 SVD analysis

The "Itc" matrix described above was used as input to the SVD algorithm. The SVD program takes the Itc

1. The SMART system (version 1 1.0) was made available through

the SMART group at Cornell University. Chris Buckley was

especially generous in consultations about how to get the software to do somewhat non-standard things.

106
transformed term-document matrix as input, and

calculates the best "reduced-dimension"

approximation to this matrix. The result of the SVD

analysis is a reduced-dimension vector for each term and each document, and a vector of the singular

values. The nimiber of dimensions, k, was between

200 and 300 in our experiments. This reduceddimensional representation is used for retrieval. The

cosine between term-term, document-document, or term-document vectors is used as the measure of

similarity between them.

We were recently able to compute the SVD analysis

of the full 742k x 104k matrix described above, but not in time to include the results in this paper. For the runs submitted, we used a sample of dociunents from

the above matrix. When appropriate, the documents

that were not sampled were "folded in" to the resulting

reduced-dimension LSI space. In all cases, we used

the weights from the 742k x 104k matrix (and did not recompute them for our samples).

For the routing experiments, we used the subset of

documents for which we had relevance judgements.

There were 68385 unique documents widi relevance

judgements. The SVD analysis was computed on the relevant 68385 document x 88112 term subset of the above matrix, containing 14461782 non-zero cells. A

204 reduced-dimensional approximation took 18 hrs of CPU time to compute on a Sparc 10 workstation.

This 204-dimensional representation was used for matching and retrieval.

For the adhoc experiments, we took a random sample

of 70000 documents. A reduced-dimensional SVD

approximation was computed on a 69997 document x 82968 term matrix (7666044 non-zeros). The

resulting 199 reduced-dimensional representation was

used for retrieval. The 672331 documents not included in this sample were "folded in" to the 199- dimension LSI space, and the adhoc queries were compared against all 742k documents.

These "folded in" documents were located at the weighted vector sum of their constituent terms. That

is, the vector for a new document was computed using

the term vectors for all terms in the document. For

documents that are actually present in the term- document matrix, this derived vector corresponds

exactly to the doomient vector given by the SVD.

New terms can be added in an analogous fashion. The

vector for new terms is computed using the document

vectors of all documents in which the term appears. When adding documents and terms in this manner, we

assume that the derived "semantic space" is fixed and

that new items can be fit into it. In general, this is not

the same space that one would obtain if a new SVD

were calculated using both the original and new

documents. In previous experiments, we found that sampling and scaling 50% of the documents, and

"folding in" the remaining documents resulted in performance that was indistinguishable fi^om that observed when all documents were scaled. Here,

however, the scaling is based on less than 10% of the

total corpus. We also had (from TREC-1) LSI analyses of the 9

subcoUections in CD-I and CD-2.

3.3 Queries and retrieval

Queries were automatically processed in the same way

as documents. For queries derived from the topic statement, we began with the full text of each topic

(all topic fields), and stripped out the SGML field

identifiers. For feedback queries, we used the full text of relevant documents. A query vector (or new

document in the case of routing) indicating the frequency with which each term appears in the query was automatically generated for each topic. The

query was transformed using SMART'S "Itc" weighting.

Note that we did not use any Boolean connectors or proximity operators in query formulation. (The

implicit connectives, as in ordinary vector methods,

fall somewhere between ORs and ANDs, but with an

additional kind of "fuzziness" introduced by the dimension-reduced association matrix representation

of terms and docimients.)

The terms in the query are used to identify a vector in the LSI space; recall that each term has a vector

representation in the space. A query is simply located

at the weighted vector sum of its constituent term

vectors. The cosine between the query vector and

every document vector is computed, and doc\mients

are ranked in decreasing order of similarity to the query. (Although there are many fewer dimensions

tiian in standard vector retrieval, the entries are almost

all non-zero so inverted indices are not useful. This means that each query must be compared to every document.)

For 200-dimensional vectors, about 60,000 cosines can be computed per minute on a Sparc2. This time

includes both comparison time and ranking time, and

assumes that all document vectors are pre-loaded into memory. For the adhoc queries, the time to compare a query to the 743k documents was about 10 minutes if

107
all comparisons were sequential. It is, however,

straightforward to spUt this matching across several machines or to use parallel hardware since all documents are independent. Preliminary experiments

using a 16,(XX) PE MasPar showed that 60,000 cosines

could be computed and sorted in less than 1 second.

It is important to note that all step in the LSI analysis are completely automatic and involved no human

intervention. Documents are automatically processed

to derive a term-document matrix. This matrix is decomposed by the SVD software, and the resulting

reduced-dimension representation is used for retrieval. While the SVD analysis is somewhat costly in terms of time for large collections, it need is computed only once at the beginning to create the reduced-dimension

database. (The SVD takes only about 2 minutes on a SparclO for a 2k x 5k matrix, but this time increases to about 18 hotirs for a 60k x 80k matrix.)

3.4 TREC-2: Routing experiments

For the routing queries, we created a filter or profile for each of the 50 training topics. We submitted

results from two sets of routing queries. In one case, the filter was based on just the topic statements - i.e., we treated the routing queries as if they were adhoc

queries. The filter was located at the vector sum of the terms in the topic. We call these the routing_topic

(Isirl) results. This method makes no use of the

training data, representing the topic as if it was an adhoc query. In the other case, we used information

about relevant documents from the training set. The

filter in this case was derived by taking the vector sum

or centroid of all relevant documents. We call these

the routing_reldocs (IsirZ) results. There were an

average of 328 relevant documents per topic, with a range of 40 to 896. This is a somewhat unusual

variant of relevance feedback; we replace (ratiier than

combine) the original topic with relevant documents,

and we do not downweight terms that appear in nonrelevant documents. These two extremes provide

baselines against which to compare other methods for combining information from the original query and

feedback about relevant documents. In both cases, the

filter was a single vector. New documents were matched against the filter vector and ranked in decreasing order of similarity.

The new documents (336306 documents from CD-3)

were automatically processed as described in section

3.2 above. It is important to note that only terms from

the CD-I and CD-2 training collection were used in indexing these documents. Each new document is located at the weighted vector sum of its constituent

term vectors in the 204-dimension LSI space (in just the same way as queries are handled). New

documents were compared to each of the 50 routing

filter vectors using a cosme similarity measure in 204-dimensions. The 1000 best matching documents

for each filter were submitted to NIST for evaluation.

3.4.1 Results

The main results of the Isirl and lsir2 runs are shown

in Table 1 . The two nms differ only in how the profile vectors were created - using the weighted average of

the words in the topic statement for Isirl routing_topic, and using the weighted average of all relevant documents from the training collection (CD-I

and CD-2) for lsir2 routing_reldocs. Not

surprisingly, the lsir2 profile vectors which take advantage of the known relevant documents do better than the Isirl profile vectors that sunply use the topic statement on all measures of performance. The

improvement in average precision is 31% (.2622 vs. .3442). Users would get an average of 1 additional relevant document in the top 10 returned using the

lsir2 method for filtering.

Table 1

Isirl lsir2 rl+r2

(topic wds) (rel docs) (sum rl r2)

ReLret 6522 7155 7367

Avg prec .2622 .3442 .3457 Prat 100 .3799 .4524 .4394 Prat 10 .5480 .6660 .6620

R-prec .3050 .3804 .3786

Q >= Median 27(4) 40(9) 42(6)

Q < Median 23(0) 10 (0) 8(0)

Table 1: LSI Routing Results. Comparison of topic

words vs. relevant documents as routing filters.

Compared to other TREC-2 systems, LSI does

reasonably well, especially for the routing_reldocs

(lsir2) run (and the rl+r2 run to be discussed below).

In the case of lsir2, LSI is at or above the median

performance for 40 of the 50 topics, and has the best score for 9 topics. LSI performs about average for the routing_topic (Isirl) run even though no information

from the training set was used m forming the routing

vectors m this case (except, of course, for the global term weights).

We have also performed similar comparisons between

108
query vectors representing the words in queries and

the centroid of all relevant documents for some of the standard IR test collections (Med, QSI, Ganfield,

CACM, Time). In these cases, we found an average improvement of 107% when the query was replaced

by the centroid of all relevant documents. The

improvement was 67% when the top three relevant documents were used, and 33% when just the first relevant document was used. The smaller advantages

observed in TREC-2 are partially due to statistical

artifacts, and partially to the TREC topics which are much richer need statements than the usual IR queries.

(We also examined topic and reldocs profiles in TREC-1. Somewhat surprisingly, the query using just the topic terms was about 25% more accurate than the query using relevant documents from training. This is attributable to the small number and inaccuracy of

relevance judgements in the initial training set for TREC-1. This had substantial impact on performance

for some topics because our reldocs queries were

based only on the relevant articles and ignored the

original topic description.)

The Isirl and lsir2 runs provide baselines against

which various combinations of query information and

relevant docimient information can be measured. We

have tried a simple combination of the Isirl and lsir2 profile vectors, in which both components have equal

weight That is, we took the sum of the Isirl and lsir2 profile vectors for each of the topics and used this as a profile vector. The results of this analysis are shown

in the third column of the table labeled rl+r2. This

combination does somewhat better than the centroid of

the relevant documents in the total number of relevant documents returned and in average precision. (We

returned fewer than 1000 documents for 5 of die

topics and not all documents returned by the rl+r2

method had been judged for relevance, so we suspect

that performance could be improved a bit more.) For 27 of the topics, rl+r2 was better than the maximum

of the other two methods. It was never more than

about 10% worse than the best method. Thus it appears that this combination takes advantage of the best of both methods.

The rl+r2 method which combines a query vector with a vector representing the centroid of all relevant documents is a kind of relevance feedback. This is an unmual variant of relevance feedback since all the words in relevant documents are used, words in nonrelevant documents are not down-weighted, and query

terms are not re-weighted. Interestingly, this method

appears to produce improvements that are comparable

to those obtained by Buckley, Allan and Salton (1993)

using more traditional relevance feedback methods.

Average precision for the rl+r2 method is 31% better than for Isirl which used only the topic words (.3457

vs. .2622), and this is quite similar to the 38%

improvement reported by Buckley, Allan and Salton

(1993) for their richest routing query expansion

method.

The lsir2 method is generally better than the Isirl method, but there is substantial variability across

topics. The topics on which diere are the largest differences are generally those in which the cosine

between the the Isirl and lsir2 topic vectors are

smallest. The cosines between corresponding topic vectors range from .87 to .54. The lsrr2 method is substantially better on topics: 71 (incursions by

foreign miUtary or guerrilla groups), 73 (movement of

people from one country to anotiier), 87 (criminal

actions against officers of failed financial institution).

94 (crime perpetrated with die aid of a computer), 98

(production of fiber optics equipment). There are a few topics for which Isirl is substantially better than

lsir2: 63 (machine translation system), 65 (information

retrieval system), 85 (actions against corrupt pubUc

officials), 95 (computer application to crime solving).

It is not entirely clear what distinguishes between

these topics, especially topics 94 and 95, for example.

We have not yet had time to look in detail at the

failures of the LSI system. We will examine both

misses and false alarms in more detail. A preliminary

examination of a few topics suggests that lack of

specificity is die main reason for false alarms (highly

ranked but irrelevant documents). This is not

surprising because LSI was designed as a recaU- enhancing method, and we have not added precisionenhancing tools altiiough it would be easy to do so. We would also like to examine some query spUtting

ideas. We have previously conducted experiments

which suggest that performance can be improved if the filter is represented as several separate vectors. We

did not use this method for die TREC-2 results we

submitted, but would like to do so. (See also KaneEsrig et al., 1991 or Foltz and Dumais, 1992, for a discussion of multi-point interest profiles in LSI.)

3.5 TREC-2: Adhoc experiments

We submitted two sets of adhoc queries - Isiasm and

Isial. We had mtended to compare the new SMART

pre-processing (Isiasm) and a single LSI space (Isial)

with our old TREC-1 pre-processing and 9 separate

subcollection spaces. Unfortunately, there were some

serious errors in our translation between internal document numbers and Uie <DOCNO> labels

109
(documents not in the LSI scaling were mislabeled), so the Isial run results are incomplete and misleading.

We have corrected this translation problem, and the correct results are labeled Isial*. These results are summarized in Table 2. We have not yet completed

the comparison against the 9 separate subspaces from

TREC-1.

Table 2

Isiasm Isial errorIsial* correct

ReLret 7869 4756 6987

Avg prec .3018 .1307 .2505

Prat 100 .4306 .2664 .3922

Prat 10 .5020 .3340 .5100

R-prec .3580 .1937 .3069

Q >= Median 37(2) 16(1) 25(1)

Q< Median 13(0) 34(7) 25 (0)

Table 2: LSI Adhoc Results. Comparison of

standard vector method with LSI (corrected

version, but missing relevance judgements).

In terms of absolute levels of performance, both

Isiasm and Isial* are about average. The SMART

results (Isiasm) are somewhat worse than the TREC-2

SMART results reported by Buckley et al., Fuhr et al., or Voorhees. but this is because we used sUghtly

different pre-processing options and did not include

phrases. Although it is generally difficult to compare

across systems, the SMART (Isiasm) and LSI (Isial*) runs can meaningfully be compared since both use the same pre-processing. The starting term-document

matrix was the same in both cases. Much to our

disappointment, the reduced-dimension LSI

performance appears to be somewhat worse than the comparable SMART vector method. However, it is important to reaUze that many of the documents

returned by Isial* were not judged for relevance

because they were not submitted as an official run. Table 3 shows the number of documents for which

there are no judgements. Consider the results for just the top 100 documents for each query (i.e., the documents judged by the NIST assessors). For Isiasm,

all 5000 documents were judged since this was an

official run, and 2153 were relevant. For Isial*, only

4073 docmnents were judged and almost as many,

2122, were relevant. Thus, if only 31 of the 927

unjudged Isial* documents are relevant LSI

performance would be comparable to SMART

performance, and if more than 31 were relevant LSI

performance would be somewhat better. Sunilarly for

the toplOOO documents, Isial* had more than 4000

more documents without relevance judgements than

did Isiasm.

Table 3

Isiasm Isial* Isiasm Isial* toplOO toplOO toplOOO toplOOO

relevant 2153 2122 15559 12230

not-relevant 2847 1961 7869 6987

not-judged 0 927 26572 30694

Table 3: Summary of missing relevance

judgements for standard vector method and LSI.

Because the missing relevance judgements make

direct comparisons between SMART and LSI difficult, we decided to look at performance for just the documents for which we had relevance judgements.

That is, we looked at performance considering just the 38175 unique documents for which we have adhoc

relevance judgements. These results are shown m

Table 4.

Table 4

Isiasm Isial* 38175 38175

ReLret 9493 9596

Avg prec .3700 .3789 Prat 100 .4306 .4466 Prat 10 .5020 .5220

R-prec .3977 .3995

Table 4: LSI Adhoc Results. Comparison of

standard vector method with LSI using only

documents for which relevance judgements were

available.

The most strikmg aspect of these results is the higher

overall levels of performance. This is to be expected

since we are only considering the 38175 documents

for which we have relevance judgements, and there are 700k fewer documents than in the official results. Considering only this subset of documents, there is a

small advantage for LSI compared to the SMART

vector method. Taken together with the results for just the top 100 documents these results suggest that LSI can outperform a straightforward vector method.

We were somewhat disappointed at the relatively

110
small difference between LSI and a comparable vector method in the TREC enviroimient, given that we have

consistently observed larger advantages previously.

The most likely reason for this is that the TREC topics are much richer and more detailed descriptions of

searchers' needs than are found in typical JR requests.

The average TREC query has 51 unique words in it, and many of these are very specific names. Since LSI

is primarily a recall enhancing method it has little effect on these already very rich queries. This is much

the same conclusion that groups who tried various

kinds of query expansion (another recall enhancing

method) reached - e.g., see Voorhees 1993. We tried one analysis using the new "summary" field for each topic. This field alone is used as a much

shorter topic statement (often quite similar to the description field) that covers the relevance

assessments. These resxilts are summarized in Table

5. As expected, overall performance is lower than

with the complete topics. More interestingly, the difference between LSI and the standard vector method is now larger - 16% in average precision. This

is still a somewhat smaller advantage than we have

seen in previous experiments with smaller test collections, but even the summary queries have an

average of 1 1 imique words in them.

Tables

Isiasm Isial* 38175 38175

ReLret 8043 8676

Avg prec .2589 .3008

Prat 100 .3344 .3710

Prat 10 .3420 .3680

R-prec .3114 .3386

Table 5: LSI Adhoc Results - Summary Topics.

Comparison of standard vector method with LSI

using only documents for which relevance

judgements were available, using only the

Summary field.

The reduced dimension LSI vector retrieval method

can offer performance advantages compared to a

standard vector method for the large TREC collection. The advantages are larger with shorter queries, as expected. The exact nature of this advantage (e.g., which documents are retrieved by LSI but not the standard vector method) needs to be examined in more

detail.

4. Improving performance

4.1 Improving performance - Speed

The LSI/SVD system was built as a research prototype

to investigate many different information retrieval and

interface issues. Retrieval efficiency was not a central concern because we first wanted to assess whether the method worked before worrying about efficiency, and

because the initial appUcations of LSI involved much

smaller databases of a few thousand documents.

Almost no effort went into re-designing the tools to work efficientiy for the large TREC databases.

4.1.1 SVD

SVD algorithms get faster all the time. The sparse,

iterative algorithm we now use is about 100 times

faster than tiie method we used initially (Berry, 1992).

There are the usual speed-memory tradeoffs in die SVD algorithms, so time can probably be decreased some by using a different algorithm and more

memory. Parallel algoritimis will help a littie, but

probably only by a factor of 2. Finally, all calculations are now done in double precision, so both

time and memory could be decreased by using single

precision computations. Preliminary experiments with

smaller IR test collections suggest that this decrease in precision will not lead to numerical problems for the SVD algoritiim. It is important to note diat the pre- processing and SVD analyses are one-time-only costs

for relatively stable domains.

4.1.2 Retrieval

Query vectors are compared to every document. This

process is linear in the number of documents in the database, and can be qmte slow for large databases. Although there are no practical and efficient algorithms for finding nearest neighbors in 200- or 300-dimensimal spaces, there are several methods

which could be used to speed retrieval, a) Document

clustering could be used to reduce the number of

comparisons, but accuracy would probably suffer some. We have explored several heuristics for

clustering, but none are particularly effective when

high levels of accuracy are maintained, b) HNC's

MatchPlus system (Gallant et al., 1993) uses another

approach to reduce the number of alternative documents that must be matched. They use an initial keyword match to eliminate many documents and

calculate reduced-dimension vector scores for only the subset of documents meeting the initial keyword

restriction. This may be a reasonable alternative for long queries (like TREQ, but we beUeve that recall would be reduced for short queries. In addition two

111
data structures need to be maintained, c) Query

matching can also be improved tremendously by

simply using more than one machine or parallel

hardware. Using a 16,000 PE MasPar, with no

attempt to optimize the data storage or sorting, we

decreased the time required to match a 200-

dimensional query vector against all document vectors and sort by a factor of 60 to 100.

4.2 Improving Performance - Accuracy

We have only begun to look at a large number of

parametric variations that might improve LSI

performance. One important variable for LSI retrieval

is the number of dimensions in the reduced dimension

space. In previous experiments we have found that performance improves as the number of dimensions is increased up to 200 or 300 dimensions, and decreases

slowly after that to the level observed for the standard vector metiiod (Dumais, 1991). We have examined

TREC-2 performance using fewer dimensions than

reported above (204 for the routing queries and 199

for the adhoc queries) and consistently found worse

performance. Thus, it looks like we could improve

performance simply by increasing the number of

dimensions some. Unfortunately, this requires

rerunning the SVD.

We also noticed that many of the adhoc queries

contained "NOTS". Since LSI does not use any

Boolean logic and represents a query as the vector sum of its constituent terms, we thought that removing

this information might help. We modified the topic statements by hand to remove negated phrases.

Performance improved by less that 2%.

We still need to experiment with different term

weighting metiiods. For the routing and adhoc

experiments we used SMART'S "Itc" weighting for both the corpus of documents and the queries.

Buckley and Salton's TREC-1 paper suggests that alternative weightings may be more effective for the

large TREC document collection. Reweighting the query vectors is easy. Reweighting the document

collection is more difficult, because this changes the term-document matrix and a new SVD is required.

For the routiog queries we would like to try several

alternative methods of combining information from

the original query and the relevant documents to take

better advantage of the good training data that is available. We expect term re-weighting and the use of

negative information (e.g., down weighting terms from non-relevant documents) to improve

performance some.

In order to better understand retrieval performance we

have begun to examine two kinds of retrieval failures: false alarms, and misses. False alarms are documents

that LSI ranks highly that are judged to be irrelevant. Misses are relevant documents that are not in the top

iOOO returned by LSL

4.2.1 False Alarms.

The most common reason for false alarms was lack of

specificity. These highly ranked but irrelevant articles were generally about the topic of interest but did not meet some of the restrictions described in the topic

statement. Many topics required this kind of detailed processing or fact-finding that the LSI system was not designed to address. Precision of LSI matching can be

iucreased by many of the standard techniques - proper noim identification, use of syntactic or statisticallyderived phrases, or a two-pass approach involving a standard initial global matching followed by a more

detailed analysis of the top few thousand documents.

Buckley and Salton (1992, SMART'S global and local matching), Evans et al. (1992, (XARTT's evoke and

discriminate strategy). Nelson (1992, ConQuest's

global match followed by the use of locahty of

information), and Jakobs, Krupka and Rau (1992,

GE's pre-filter followed by a variety of more stringent

tests) all used two-pass approaches to good advantage

in TREC-1 or TREC-2. We would like to try some of

these methods, and will focus on general-purpose,

completely automatic methods that do not have to be

modified for each new domain or query restriction.

Another possible reason for false alarms appears to be

the result of inappropriate query pre-processing. The

use of negation is the best example of this problem.

32 of 50 adhoc queries contain some negation in the

topic statement. Some preliminary experiments

(described briefly above) found only a small

improvement in performance when negated

information was manually removed from the topics.

Another example of inappropriate query processing

involved the use of logical connectives. LSI does not handle Boolean combinations of words, and often returned articles covering only a subset of ANDed

topics. Often one aspect of the query appears to dominate (typically the one described by the terms with high weights). Limiting the contribution of any one term to the overall similarity score might help this problem.

Finally, it is not at all clear why about 20% of the false alarms were returned by LSI. Since LSI uses a

statistically-derived "semantic" space and not

surface-level word overlap for matching queries to

112
documents, it is sometimes difficult to understand why

a particular document was returned. One advantage of

the LSI mediod is that docimients can match queries even when they have no words in common; but this can also produce some spurious hits. Another reason

for false alarms could be inappropriate word sense disambiguation. LSI queries are located at the weighted vector sum of the words, so wcffds are "disambiguated" to some extent by the other query

words. Similarly, the initial SVD analysis used the context of other words m articles to determine the location for each word in the LSI space. However,

since each word has only one location, it sometimes

appears as if it is "in the middle of nowhere". A

related possibility concerns long articles. Lengthy

articles which talk about many distinct subtopics were

averaged into a single document vector, and this can sometimes produce spurious matches. Breaking larger

documents into smaller subsections and matching on

these might help.

4.2.2 Misses.

For this analysis we will examine a random subset of

relevant articles that were not in the top 1000 returned by LSI. Many of the relevant articles were fairly

highly ranked by LSI, but there were also some

notable failures that would be seen only by the most

persistent readers. So far, we have not systematically

distinguished between misses tiiat "almost made it" and those that were much further down the Ust.

Most of the misses we examined, represent articles that were primarily about a different topic than the query, but contained a small section that was relevant

to the query. Because documents are located at the average of their terms in LSI space, they will generally be near the dominant theme, and this is a

desirable feature of the LSI representation. Some kind

of local matching should help m identifying less central themes in documents.

Some misses were also attributable to poor text (and

query) pre-processing and tokenizatioa

4.3 Open issues

On the basis of preliminary failure analyses we would

like to exploring some precision-enhancing methods.

We would also like to explore three additional areas.

4.3.1 Separate vs. combined scaling

We used 9 separate subscalings for the TREC-1

experiments. For TREC-2 we used a single scaling

(based on a very small sample). We have also

recently finished a complete scaling and will compare

this with the subcollection scalings and tiie sampled

full scaling.

4.3.2 Centroid query vs. many separate

points of interest

A single vector was used to represent each query. In some cases the vector was the average of terms in the

topic statement, and in other cases the vector was die average of previously identified relevant doamients.

A single query vector can be inappropriate if interests are multifacted and these facets are not near each

other in the LSI space. We have developed techniques

tiiat allow us to match using a controllable compromise between averaged and separate vectors (Kane-Esrig et al., 1991). In the case of the routing

queries, for example, we could match new documents

against each of the previously identified relevant documents separately rather than against their average.

4.3.3 Interactive interfaces

All LSI evaluations were conducted using a noninteractive system in essentially batch mode. It is well known that one can have tiie same imderlying retrieval and matching engine, but achieve very different

retrieval success using different interfaces. We would

like to examine the performance of real users with

interactive interfaces. A number of interface features could be used to help users make faster (and perhaps more accurate) relevance judgements, or to help them

explicitiy reformulate queries. (See EHmiais and

Schmitt, 1991, for some preliminary results on query

reformulation and relevance feedback.) Another

interestmg possibility involves returning something

richer than a rank-ordered Ust of doamients to users. For example, a clustering and graphical display of the top-k documents might be quite useful. We have done some preliminary experiments using clustered return

sets, and would like to extend this work to the TREC

collections.

The general idea is to provide people witii useful

interactive tools that let them make good use of their knowledge and skills, rather than attempting to build

all the smarts into the database representation or matching components of the system.

113
5. Onward to TREC-3

We were quite pleased that we were able to use many

of the existing LSI/SVD tools on the TOEC-l and

TREC-2 collections. The most important finding in

this regard was that the large, sparse SVD problems

could be computed widiout numerical or convergence

problems. We modified the preprocessing

substantially for TREC-2, now have many of the basic

tools in place and should be able to conduct more

experiments comparing various indexing and query

matching ideas using the same underlying LSI engine.

Bigger SVDs, faster query matching, improving

precision, and interactive interface issues are the major areas targeted for improvement.

6. References

[1] Berry, M. W. Large scale singular value

computations. International Journal of

Supercomputer Applications, 1992, 6(1). 13- 49.

[2] Buckley, C, Allan, J., and Salton, G. Automatic routing and ad-hoc retrieval using

SMART: TREC 2. To appear in: Proceedings

ofTREC-2.

[3] Buckley, C. and Salton, G. Automatic

retrieval with locality information using

SMART. In D. Harman (Ed.) The First Text

REtrieval Conference (TREC-1 ), NIST Special

Publication 500-207, 1992, 59-72.

[4] Cullum, J.K. and Willoughby, R.A. Lanczos

algorithms for large symmetric eigenvalue

computations - Vol 1 Theory, (Qiapter 5: Real

rectangular matrices). Brikhauser, Boston,

1985.

[5] Deerwester, S., IXmiais, S. T., Landauer, T.

K., Furnas, G. W. and Harshman, R. A.

Indexing by latent semantic analysis. Journal

of the Society for Information Science, 1990,

41(6),391-4(n.

[6] Dumais, S. T. Improving the retrieval of

information from external sources. Behavior

Research Methods, Instruments and

Computers, 1991, 23(2), 229-236.

[7] Dumais, S. T. LSI meets TREC: A staus

report. In D, Harman (Ed.) The First Text REtrieval Conference (TREC-I). NIST special

pubUcation 500-207, 137-152.

[8] Dumais, S. T. and Schmitt, D. G. Iterative searching in an online database. In Proceedings of Human Factors Society 35th

Annual Meeting, 1991, 398-402.

[9] Evans, D., Lefferts, R., Grefenstette, G.. Handerson, S.. Hersh, W., and Archbold, A. CXARTT TREC Design, experiments and

results. In D. Harman (Ed.) The First Text

REtrieval Conference (TREC-1), NIST Special

Publication 500-207, 1992. 251-286.

[10] Foltz, P. W. and Dumais, S. T. Personalized

information delivery: An analysis of

information filtering methods.

Communications of the ACM, Dec. 1992,

35(12), 51-60.

[11] Furnas, G. W., Deerwester, S., Dumais, S. T., Landauer, T. K., Harshman, R. A., Streeter, L.

A., and Lochbaum, K. E. Information retrieval using a singular value decomposition model of

latent semantic structure. In Proceedings of

SIGIR, 1988, 465-480.

[12] Gallant, S., Hecht-Nielson, R., Caid, W., Qing,

K., Carleton, J., Sudbeck, D. TIPSTER Panel - HNC's MatchPlus System. In D. Harman

(Ed.) The First Text REtrieval Conference

(TREC-1), NIST Special Publication 500-207,

1992, 107-112.

[13] Jacobs, P, Krupka, G., and Rau, L. A Boolean

approximation method for query construction

and topic assignment in TREC. In D. Harman

(Ed.) The First Text REtrieval Conference

(TREC-1), NIST Special Publication 500-207,

1992, 297-308.

[14] Kane-Esrig, Y., Streeter, L., Dumais, S. T., Keese, W. and Casella, G. The relevance

density method for multi-topic queries in ioformation retrieval. In Proceedings of the 23rd Symposium on the Interface, E. Keramidas (Ed.), 1991. 407-410.

[15] Nelson, P. Site report for the Text REtrieval

Conference. In D. Harman (Ed.) The First Text REti-ieval Conference (TREC-1), NIST

Special Publication 500-207, 1992, 287-296.

[16] Salton, G. and McGill, M.J. Introduction to Modern Information Retrieval. McGraw-Hill,

1983.

[17] Voorhees, E. On expanding query vectors with lexically related words. To appear in: Proceedings ofTREC-2.

114
7. Appendix

Latent Semantic Indexing (LSI) uses singular-value

decomposition (SVD), a technique closely related to eigenvector decomposition and factor analysis

(Cullum and Willoughby, 1985). We take a large

term-document matrix and decompose it into a set of

it , typically 100 to 300, orthogonal factors from which

the original matrix can be approximated by linear combination.

More formally, any rectangular matrix, X, for example atxd matrix of terms and documents, can be

decomposed into the product of three other matrices: X = To'So'Dq',

rxr rxr rxd

such that To and Do have orthonormal columns, .So is diagonal, and r is the rank of X. This is so-called singular value decomposition of X

If only the k largest singular values of So are kept

along with their corresponding columns in the To and

Do matrices, and the rest deleted (yielding matrices S , T and D ), the resulting matrix, X , is the imique matrix

of rank k that is closest in the least squares sense to X

X^^X = T-S-p'

The idea is that the X matrix, by containing only the

first k independent linear components of X , captures

the major associational structure in the matrix and

throws out noise. It is this reduced model, usually

with ^: « 100, that we use to approximate the term to docimient association data in X . Since the number of

dimensions in the reduced model (k) is much smaller

than the number of unique terms (t), minor differences in terminology are ignored. In this reduced model, the closeness of documents is determined by the overall pattern of term usage, so documents can be near each other regardless of the precise words that are used to describe them, and their description depends on a kind of consensus of their term meanings, thus dampening the effects of

polysemy. In particular, this means that documents

which share no words with a user's query may stUl be

near it if that is consistent with the major patterns of

word usage. We use the term "semantic" indexing to describe our method because die reduced SVD

representation captures the major associative relationships between terms and documents.

One can also interpret the analysis performed by SVD

geometrically. TTie result of the SVD is a kdimensional vector representing the location of each

term and doomient in the /: -dimensional

representation. The location of term vectors reflects the correlations in their usage across documents. In

this space the cosine or dot product between vectors corresponds to their estimated similarity. Since both term and document vectors are represented in the same space, similarities between any combination of

terms and documents can be easily obtained.

Retrieval proceeds by using the terms in a query to identify a point in the space, and all documents are then ranked by their similarity to the query. We make

no attempt to interpret the underlying dimensions or

factors, nor to rotate them to some intuitively

meaningful orientation. The analysis does not require us to be able to describe the factors verbally but

merely to be able to represent terms, documents and

queries in a way that escapes the unreliabiUty,

ambiguity and redundancy of individual terms as descriptors.

(Hioosing the appropriate number of dimensions for the LSI representation is an open research question.

Ideally, we want a value of k that is large enough to fit aU the real structure in the data, but small enough so

that we do not also fit the sampling error or unimportant details. If too many dimensions are used,

the method begins to approximate standard vector methods and loses its power to represent die similarity

between words. E too few dimensions are used, there

is not enough discrimination among similar words and

documents. We find that performance improves as k

increases for a while, and then decreases (Dumais,

1991). That LSI typically works well with a relatively

small (compared to the number of unique terms)

number of dimensions shows that these dimensions

are, in fact, capturing a major portion of the meaningful structure.

115

An Information Retrieval Test-bed on the CM-5

Brij Masand and Craig Stanfill Thinking Machines Corporation

245 First Street Cambridge MA 02142

I. INTRODUCTION

For many years, research on information retrieval was

mostly confined to a few relatively small test collections such

as the Cranfield collection [1], die NPL Collection [2], and the CACM Collection [3]. Over the years, results on diose collec- tions accumulated, with the aim of determining which tech- nique or combination of techniques resulted in the best

precision/recall figures on those collections. Gradually, a "standard model" more-or-less emerged: for the test collections under study, consistently good results are obtained by

vector-model retrieval using a cosine similarity measiue, tf.idf weighting, and a stemming algorithm (e.g. Chapter 9 of [4], [5]).^ Out-performing this model on the old test collections

has proved extremely difficult. This has led to a danger of

stagnation in the field of IR, and a feeling that the majority of

what can be learned from precision-recall experiments on the old collections has been learned.

Fortunately for the field, a number of recent developments

have led to new challenges for the field. Among tiiese:

• The Hpster project [6] has led to the construction of a test collection of unprecedented size [7]. This leads to chal- laiges related to scaling up IR metiiods.

• IR methods are now being used on full documents (e.g. news stories), rather than on abstracts. This leads to new

challenges relating to the structure of laige documents,

particularly effects relating to term proximity.

• The on-line database vendors have shown interest in full- text IR. This leads to challenges relating to the integration

of IR methods with traditional boolean methods which, in the operational environment, must continue to be sup- ported if only for the benefit of the existing, entrenched

user community.

1. Other ^preaches yield results which are nearly as good and, in rare cases better. Approaches based on Bayesian inference networks

are particularly notable in that respect. However, the above model

has come to be accepted as a de-facto standard against which new

methods are measured.

Connection Machine is a registered trademark of Thinking Machines Corporation. CM, CM-2, CM-5, and Datavault are trademarits of Thinking Machines

Corporation. SPARC is a trademaiic of Sun Microsystems.

• Network-oriented IR protocols have become increasingly

popular as a basic tool for navigating across die network.

This leads to the challenge of designing systems which

give uniformly interpretable results across a distributed database. The work reported in this paper represents some steps along

die way to solving Uiese problems. In essence, the challenge we are facing is to design a system which delivers high precision-recall figures on large databases of long, complex docu- ments. Furthermore, the system must be compatible with

existing boolean retrieval methods, and it must be possible to use this system in a distributed database in a manner which

yields consistentiy interpretable results. The specific steps

reported at this point consist of the following:

• A database architecture which is suitable for use on large

collections of structured documents, which supports both

base-line IR and boolean retrieval mediods.

• An in-memory implementation of this architecture on a massively parallel computer (the Connection Machine

model CM-5), which is used as test-bed.

• Precision-Recall figures for this test-bed applied to die Tipster collection, as part of TREC.

It must be emphasized that this work is in an early stage

and, at this point, has not reached the point where tiiese mediods are demonstrable on extremely large databases. Further- more, work relating to takmg advantage of document structure has only barely begun.

n. DATABASE ARCHITECTURE

A. Choice ofa Representation

There are three basic approaches to representing databases

for retrieval applications:

• Text Files. In diis method, the database is stored in full- text form, and scanned sequentially. Such mediods are often implemented by special-purpose hardware [8].

• Signature Files. In tiiis family of methods, a compressed

surrogate for the text file is searched instead of the text file

itself. Overlap encoding is usually employed to constiuct

the surrogate. There are many variants on signature files

[9].

• Inverted Files. In this family of methods, an index containing the locations of every word in the database is constructed. The search is then accompUshed by reference to

117
the index. Again, there are many variants on inverted file representations [10]. Inverted files have proved the only technique which supports interactive access to very large databases; this is primarily due to the excessive I/O requirements of the other

methods. Within the family of inverted file methods, several

variants are possible:

• Simple Inverted Files. In a simple inverted file, the index

consists of a list of documents in which each word occurs.

• Inverted Files with Weights. In an inverted file with

weights, the above information is supplemented with

weights, in support of vector retrieval methods.

• Structured Inverted Files. In a structured inverted file, the index captures structural information (typically the paragraph/sentence/word coordinates of each occiarrence of each term), in support of boolean retrieval methods

which rely on proximity operators (e.g. word adjacency).

In this research, we have decided to explore the use of

structured inverted files for the following reasons:

• They support die proximity operations required as part of

boolean retrieval systems. This support makes architec- tures based on structured inverted files more likely to be

adopted by the major on-line vendors.

• They provide a fundamentally richer representation of

document structure than is available with the other methods.

• They are collection-independent and retrieval-method

independent.

This last point requires some explanation. In a distributed environment, it would be useful to be able to search text files at multiple locations as if they were a single text file. Once

weights — which are both collection-dependent and retrieval- method dependent — are incorporated into the index, trans- parent distributed access becomes impossible. The product of

this is that the results of a single query applied to multiple

databases cannot be meaningfully combined, since there is no way to compare the ranks or scores applied to the documents

returned. One of the primary challenges associated with the use of

structured inverted files is their bulk: there are approximately

as many index entries in the inverted file as there are words in the database, and each entry must represent a document, para- graph, sentence, and word coordinates for that word. This

problem has been substantially solved by use of a novel combination of compression techniques [11], which allow struc- tured indexes having a bulk on the order of 1/3 the size of the

full text to be constructed. The second challenge associated widi the use of structured inverted files is to implement the standard information

retrieval model using them. The results of this effort are reported in diis paper.

The final challenge associated with structured inverted files is using them to implement methods which go beyond the standard model, taking into accoimt the added richness the

structured representation to improve retrieval system perfor- mance for databases having long, structured documents. This

final challenge remains a topic for future research, and there

are no results to report at this time.

B. The Database Architecture

The database consists of the following structures:

• A Compressed Structured Posting File. The first component of the database is an array of compressed postings.

Each posting gives the location of a word expressed as a document-paragraph-sentence-word 4-tuple. The postings

are sorted by word ID, in ascending document order.

• A Lexicon/Index. The second component of the database

is a lexiconMdex. For each word in the database, it stores the number of times it occurs plus the location of its postings in the posting file. The lexicon is structured so that the information pertaining to a given word may be quickly

located.

• Document Information. The final component of the data- base is an array of document-related information. Each

entry contains a mapping from an internal document identifier (an integer) to an external document identifier (a

string). Additional information, such as the length of the document or normalization information, may be added as

necessary. The following operations are supported:

• Adding Postings. A 5-tuple consisting of a word plus its four coordinates may be added to the database.

• Extracting Postings. A word is presented to the database. An array having the decompressed coordinates of all occurrences of diat word is returned. The array is sorted by

ascending document coordinate, with paragraph, sentence,

and word coordinates used as additional sort keys as needed.

• Extracting Lexicon Information. Lexicon information

such as the number of occurrences of a word may be

extracted.

• Setting Lexicon Information. Supplemental lexicon

information (e.g. part-of-speech information) may be

added to the lexicon. This feature is not used in the work

reported herein.

• Defining a Document. Defines document-specific infor- mation such as external docimient identifier, document

location on disk, etc., and associates it with a document

coordinate.

• Extracting Document Information. Extracts the document-specific information mentioned above, given a document coordinate.

118
We believe that this architecture suffices to implement

boolean retrieval methods, standard IR methods, and novel

methods making use of document structure, and that it can be

scaled to very large databases and to massively parallel computers.

C. The Test-bed

The above architecture has been implemented in test-bed form on the Connection Machine model CM-5. The purpose

of this implementation is to permit various retrieval methods

to be evaluated, rather than to support on-line services on very

large files. As such, the focus was on simplicity of implementation, speed of database construction, and speed of query exe- cution, rather than on handling files larger than a few

Gigabytes.

The CM-5 [12] is a general-purpose parallel computer constructed from commodity microprocessors. Each processing

node consists of a SPARC microprocessor, 32 megabytes of

RAM, and a network interface. The CM-5 has two user-accessible networks: a Data Router, which is used for point-topoint transmission of data packets, and a Control Network

which is used to unplement global operations such as barrier synchronization, broadcast, and global-maximum. The data router provides each processor with 5 MB/second (full

duplex) worth of point-to-point bandwidth. The control net- work is constructed using a fan-in/fan-out tree so that global

operations complete within a few microseconds of their initia- tion.The CM-5 I/O system consists of a high-performance mass

storage system called the scalable disk array (SDA). la this system, disk controllers are connected directly to the CM-5's

data router. This allows all processors to have equal access to

all disks in the system, providing the image of a scalable

shared disk environment. The file system implements a UNIX

file system on top of this hardware, such that file systems may

be striped across up to 256 disks [13]. The result is a file sys- tem which can sustain transfer rates exceeding 150 MB/second in large configurations.

The basic approach taken in this implementation is to begin

by partitioning the document set among the processors. This is done by having each processor read a fixed-size contiguous

chunk (1 MB) of data from the input file. In general, this will

result in some documents spaiming processor boundaries, so document fragments will then be re-distributed. Once this is done, each processor parses and lexes its own documents,

using conventional programming techniques. The postings are then inserted into the mverted file. One novel implementation trick is used in this phase of processing: rather than sorting the postings, which would be very

time consuming owing to the size of the imcompressed postmgs, the database is indexed in two passes. On the first pass, called the "dry run", the postings are generated, counted, and

discarded. At the end of this pass, the system knows how

much space is required to store the posting list for each word.

Space is then allocated and carved up. The second "production" phase then begins. The database is scanned, lexed. and

indexed again from scratch, but this time the postings are compressed and stored into the space allocated at the end of

the dry run. This strategy doubles indexiag time but, by elimi- nating the expense of sorting the posting file, it ends up both

simplifying the software and reducing overall database construction time. At the end of this phase, the data structures noted above

(posting file, lexicon/index, and document information) have

been constructed in-memory, and the database is ready for querying. Using these methods on a 64 processor CM-5, the TREC-2 training database (2.2 Gigabytes) required 20 minutes to index. The speed of the indexing software permits the database to be re-indexed for every retrieval experiment,

allowing both indexing methods and query methods to be conveniently explored. Ignoring stop words, the size of the compressed inverted file index for the TREC database is 24% of

the raw text. Details of the compression algorithm can be

found in [11]. The first step in query evaluation is to broadcast the query

to all processors. In a boolean system, the query would then

be processed locally by each processor, then the resiilts pro- duced by the processors concatenated to return the final answer.

In an information retrieval system based on term weighting

and docmnent ranking, slightiy more work is required. First,

query-term weights are generally based on some sort of term- frequency observations. These cannot be done locally, but

require the use of some simple global operations. For example, to determine the number of documents in which a word

occurs, each processor would count document occurrences for

itself, then the global sum operation (supported in hardware

by the Control Network) would be used to produce a machinewide count. The second problem which arises comes after the documents have been scored: an algorithm is required to

extract the highest-ranking documents in the collection. Several parallel algoridims for this task have been described in

[14]. The test-bed used a variant on the iterative extraction

algorithm: each processor locally determines its highest-ranking documents, then repeated application of the global maximum operation are used to find the best docmnents in the

collection. m. THE COSINE SIMILARITY MEASURE

Results of using die classical cosine similarity measure on

the TREC collection have already been reported elsewhere

[15], so those results have not been replicated. This section

will briefly describe how the cosine measure may be implemented within our architecture. Cosine similarity measures generally involve constructing an inverted file incorporating document-term weights. The

structured inverted file architectwe does not provide this information, pardy because it is inconsistent with the structxu-ed representation, and partly because of the difficulties noted above with regard to distributed databases. Except for

119
the cosine measure's document normalization factor, we find that it is possible to implement docimient-term weighting

based only in information in the stnictured posting file.

In general, term weights (both in queries and docmnents)

may be computed based on the following factors [5], all of

which are available at run-time in our architecture.

• Term Frequency. This is the number of times a term

occurs in the database. This information is retained in the

lexicon.

• Document-Term Frequency. This is the number of documents in which a term occurs. It may be computed at run- time by traversing posting hst for the term, coimting initial occvurences of the term.

• In-Document Frequency. This is the number of times a term occurs in a given document. This may be computed at run-time by traversing the posting list for the term, coimting the number of occurrences of the term having the same

document coordinate.

• Maximum In-Document Frequency. This is the maximum number of times a given term occurs in any docu- ment. It may be computed directly from the iu-document

frequencies.

Most conventional weighting schemes (e.g. tf.idf) may be

computed from these run- time factors. The advantage of

doing these computations at run-time is that it eliminates die need to incorporate collection- specific information into the database at indexing time. This is important for dynamic col- lections as well as for distributed databases, as described

above.

The difficulty with these methods when applied to the

cosine norm is that the total-document-weight term cannot be

conveniently computed at run-time using an inverted file. It must, therefore, be computed at mdex time and stored separately (e.g. in the document-specific information data structure). There are two solutions to this difficulty. The first is to

insist on using the cosine norm, accepting the difficulties that this implies. The second is to look for alternatives to the

cosine norm. We believe that this is the more promising

approach, but this is in the realm of work-not-yet-completed.

In any event, the value of the cosine norm for large structured documents has not been established at this time.

IV. RETRIEVAL EXPERIMENTS:

Most of the time (about 3 to 4 person months) for our TREC participation was spent on building the test bed. However we did finish some runs with automatically constructed

routing and adhoc queries for which we report the results here. The experiments were done using the entire collection. We

compare words and phrases, mixed case vs. lower case and

also explore document length normalization and proximity

measures.

A. Retrievalfor Routing Topics: All queries were constructed and optimized automatically.

The terms consisted of words (ignoring stop words) as well as

all phrases consisting of adjacent words. Numbers were

ignored and case was preserved. No stemming or query

expansion with thesauri etc. was attempted.

Routing queries were constructed by looking at each word

and (adjacent) phrase from the whole text of the topic templates and determining a weight based on the number of rele- vant documents present in the first 100 retrieved documents,

by using just that term. An initial weighted query was constructed for each topic by the above process. Then each topic

query was optimized by choosing thresholds (per topic) for

the weights and rejecting all weighted query terms below the

threshold. The optimmn threshold for each topic was chosen

by straightforward incremental search. Table 1 shows the

results for the routmg experiments. Routing queries widi both

Table 1: Routing Queries

Method

Precision at 100 docsAverage

precision

tmc6- routing-wordsphrases .3396 .2553

tmc7-routing-words .2920 .2045

tmc6-routing-wordsphrases-ip .3750 .2716

tmc6-routing-wordsphrases-doc-lengthsent-prox .3782 .2792

tmc6-routing-wordsphrases-ip-sent-prox .3856 .3344

weighted words and phrases (queryid tmc6) did better than

queries using just words (queryid tmc7). Using the same (offi- cial) queries, but adding sentence level proximity (sent-prox),

document length scaling (doc-length) and inverse weights

based on which paragraph the term appears in (ip), seem to improve results (see the next section for more details about

the techniques).

B. Retrievalfor Adhoc Topics

Adhoc queries were automatically constructed by using

words and phrases from different sections of the topic templates and using tf.idf weights (as derived fi-om the training

collection). The "best" sections for the new topics were chosen by experimenting with the training topics. Queries derived

from the description-concept sections were used for most of

the experiments. A threshold for weights was used to select terms for the final queries. Table 2 shows die results for the adhoc queries.

120
Table 2: Adhoc Queries

jyiciuou

Precision

at 100 docs

Average

precision

Case:

tmc8-adhoc-dcwp-idfcaps-wt .2002 .1276

tmcS-adhoc-dcwp-idflower-wt .1734 .1157

Document-length and

inverse-para-scaling:

tmcS-adhoc-dcwp-idflower-doc-Iength-wt .3308 .1904

tmcS-adhoc-dcwp-idfcaps-ip-wt .3432 .1939

tmc8-adlioc-dcwp-idflower-ip-wt .3422 .2027

tmc9-adhoc-etwp-idfcaps-ip-wt .3144 .1736

StemmingtmcS-adhoc-dcwp-idflower-stem-wt .1670 .1152

tmc8-adhoc-dcwp-idflower-stem-ip-wt .3240 .1980

Proximity:

tmc8-adlioc-dcwp-idfcaps-doc-length-sentprox-wt .3436 .2012

tmc8-adhoc-dcwp-idflower-doc-length-sentprox-wt .3518 .2146

tmc8-adhoc-dcwp-idfcaps-ip-para-prox-wt .2892 .1681

tmc8-adlioc-dcwp-idflower-ip-para-prox-wt .3006 .1772

tmc8-adlioc-dcwp-idfcaps-ip-sent-prox-wt .3476 .1988

tmc8-ad]ioc-dcwp-idflower-ip-sent-prox-wt .3602 .2164

121

The query tmc8 (dcwp) consisted of words and phrases

from the description and concept sections of the topic templates. Query tmc9 (etwp) used words and adjacent phrases

from the entire topic. Bold-face acronyms emphasize particular experiments with case (caps and lower), sentence and

paragraph level proximity (sent-prox and para-prox) document length scaling (doc-length), inverse weights based on

paragraph position (ip) and weight thresholds (wt). The queries were not changed for the different experiments.

Idf weighted terms from the description and concept sections taken together (query tmc8) seem to do better than those derived from the entire topic (query tmc9). C. Case

For the adhoc queries, we compared indexing with and

without preserving case (similar treatment for the queries).

Except for the simplest experiment with weight thresholds,

converting everything to lower case seems to yield comparable or better results than upper case. A similar experiment for routmg queries wasn't attempted because that would have

required reformulating and reoptimizing the routiag queries. D. Stemming

Using the Porter Algorithm software for stemming from

[16] we experimented with stemming at index time (and stem- ming the queries). We found diat stermning reduces perfor- mance when compared with similar experiments using lower

case " since the software we had used lower case. We are not

sure yet why there is such a decrease.

E. Document length and Term position

Document length scaling was used to explore the effect of

emphasizing shorter dociunents. A linear decreasing scaling

for longer documents, with a tail was used. An mverse weight

based on the paragraph the term appears in, was also explored.

Both the document length scaling and the inverse paragraph

scaling increase performance significantly and seem to be

comparable to each other.

F. Proximity

The postings for the inverted file allow use of term position.

Experiments are underway to define proximity scoring methods that enhance weights for terms appearing close together

(clusters of terms), and can also be implemented efficiendy

within the current architectiue. We have achieved good results with sentence level proximity measures based on a bonus

score for the query terms that appear within the same sentence and within a certaui distance of each other. The bonus is also proportional to the term weight itself. Experiments that used a bonus independent of term weight dramatically reduced per- formance (niunbers not reported here), possibly due to noise

introduced by clusters of unimportant terms. Similar experiments with paragraph level proximity yielded significantly

poorer results as compared to sentence level proximity.

Finally combining either document length or inverse para- graph scaling with sentence level proximity improved results.
V. CONCLUSIONS

We have successfully implemented an in-memory compressed inverted file text retrieval system on the CMS Connection Machine system.

Queries that used both words and phrases composed of

adjacent words did better that those that used words alone. While our experiments completed so far suggest that convert- ing everything to lower case for adhoc queries seems some- what better, it is not clear whether the minor differences

couldn't be removed by further optimization of other parameters. Bq)eriments that take docmnent length and term position

into accoimt suggest that normalizing for document length and

increasing the weight of terms appearing earlier in the documents lead to significant improvements for both routing and

adhoc queries. We have also seen that proximity scores based on nearby terms in a sentence improve retrieval performance.

VI. FUTURE WORK

We would like to compare the efiects of cosine normalization with what we have tried so far and also explore its inter- action with techniques that use term position and proximity

measures.

Vn. REFERENCES

[ 1 ] Qeverdon, C. W., Mills, J. & Keen, E. M. ( 1966). Factors Determining

the Performance of Indexing Systems, Vol 1 : Design, Vol. 2: Test

Results. Aslib Cranfield Research Project, Cranfield, England, 1966.

[2] Sparck Jones, K. and Webster, C.A. (1979). Research in Relevance

Weighting, British Library Research and Development Report 5553,

Computer Laboratory, University of Cambridge.

[3] Fox, E. (1983). Characteristics of Two New Experimental Collections in Computer and Information Science Containing Textual and Biblio- graphic Concepts. Technical Report TR 83-561, Cornell Univerisy:

Computing Science Department.

[4] Salton, G. (1989). Automatic Text Processing. New York: Addison-Wesley.

[5] Salton, G & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24 (5), pp. 513-523.

[6] Harman, D (1993). The DARPA TIPSTER Project. SIGIR Forum, 26

(2), pp. 26-28.

[7] Harman, D (1993). Overview of the First TREC Conference. Proceedings, SlGIR-93, pp. 36-47. Pittsburgh.

[8] Hollaar, L. A. (1992). Special-Purpose Hardware for Information

Retrieval. In Information Retrieval: Data Structures and Algorithms.

Frakes, B. & Baeze-Yates, R. eds. Englewood QiflGs, New Jersey: Prentice Hall.

[9] Faloutsos, C. (1992). Signature Files. In Information Retrieval: Data

Structures and Algorithms. Frakes, B. & Baeze-Yates, R. eds. Englewood Cliffs, New Jersey: Prentice HaU.

[10] Harman, D., Fox, E. Baeza-Yates, A. & Lee, W. (1992). Inverted Files. In Information Retrieval: Data Structures and Algorithms. Frakes, B. &

Baeze-Yates, R. eds. Englewood Cliffs, New Jersey: Prentice Hall.

[11] Linoff, G. & Stanfill, C ( 1 993). Compression of Indexes with FuU Positional Information in Very Large Text Databases. Proceedings, SIGIR93, pp. 88-95. Pittsburgh.

[12] Thinking Machines Corporation (1991). Connection Machine CM-5

Technical Summary. Cambridge, MA: Thinking Machines Corporation.

[13] Lo Verso, S., Isman, M., Nanopoulos, A., Nesheim, W., Milne, E. &

Wheeler, R. (1993). SFS: A ParaUel File System for the CM-5. Proceedings, 1993 Summer USENIX Technical Conference, pp. 291-306. Cincinnati, OH.

[14] Stanfill, C. (1992). Parallel Information Retrieval Algorithms. In Infor- mation Retrieval: Data Structures and Algorithms. Frakes, B. & BaezeYates, R. eds. Englewood Cliffs, New Jersey: Prentice Hall.

[15] Salton, G. & Buckley, C. (1992). SMART Trade-offs. Proceedings, TREC-1. Gaithersburgh, MD.

[16] Frakes, B.(1992). Stemming Algorithms. In Information Retrieval: Data Structures and Algorithms. Frakes, B. & Baeze-Yates, R. eds. Englewood Qiffe, New Jersey: Prentice Hall.

122
RECENT DEVELOPMENTS IN NATURAL LANGUAGE TEXT RETRIEVAL

Tomek Strzalkowski and Jose Perez Carballo

Courant Institute ofMathematical Sciences

New York University

715 Broadway, rm. 704

New York, NY 10003

tomek@cs.nyu.edu

ABSTRACT

This paper reports on some recent developments in our

natural language text retrieval system. The system uses advanced natural language processing techniques to enhance the effectiveness of term-based document

retrieval. The backbone of our system is a traditional statistical engine which builds inverted index files from

pre-processed documents, and tiien searches and ranks

the documents in response to user queries. Natural language processing is used to (1) preprocess the documents in order to extract content-carrying terms, (2) dis- cover mter-term dependencies and biuld a conceptual

hierarchy specific to the database domain, and (3) pro- cess user's natural language requests into effective search queries. For the present TREC-2 effort, the total of 550 MBytes of Wall Street Journal articles (ad-hoc

queries database) and 300 MBytes of San Jose Mercury

articles (routing data) have been processed, hi terms of

text quantity this represents approximately 130 milhon

words of English. Unlike in TREC-1, we were able to create a single compound index for each database, and

therefore avoid merging of results. While die general

design of the system has not changed since TREC-1

conference, we nonetheless replaced several components

and added a number of new features which are described

in the present paper.

INTRODUCTION

A typical information retrieval (IR) task is to select doamients from a database in response to a user's query, and rank these documents according to relevance. This

has been usually accomplished using statistical methods

(often coupled with manual encoding) that (a) select terms (words, phrases, and odier units) from documents

that are deemed to best represent their content, and (b) create an mverted mdex file (or files) that provide an

easy access to documents containing these terms. A sub- sequent search process wiU attempt to match a prepro- cessed user query (or queries) against term-based

representations of documents in each case determining a degree of relevance between the two which depends

upon the number and types of matching terms. Although

many sophisticated search and matching methods are

available, the crucial problem remains to be that of an

adequate representation of content for both the docu- ments and the queries.

The simplest word-based representations of content are usually inadequate since single words are rarely

specific enough for accurate discrimination, and their grouping is often accidental. A better method is to identify groups of words that create meaningful phrases,

especially if these phrases denote important concepts in database domain. For example, joint venture is an important term in the Wall Street Journal (WSJ henceforth)

database, while neither joint nor venture is important by

itself. In the retrieval experiments with the training

TREC database, we noticed that both joint and venture were dropped from the Ust of terms by the system

because their idf {inverted document frequency) weights were too low. hi large databases, such as TIPSTER, die use of phrasal terms is not just desirable, it becomes

necessary. An accurate syntactic analysis is an essential prerequisite for selection of phrasal terms. Various statistical methods, e.g., based on word co-occurrences and mutual

information, as well as partial parsmg techniques, are prone to high error rates (sometimes as high as 50%),

turning out many unwanted associations. Therefore a good, fast parser is necessary, but it is by no means

sufficient. While syntactic phrases are often better indi- cators of content than 'statistical phrases' -- where words

are grouped solely on die basis of physical proximity

(e.g., "college junior" is not the same as "junior college") " the creation of compound terms makes term matching

process more complex since in addition to die usual problems of synonymy and subsumption, one must deal with their structure (e.g., "college junior" is tiie same as

"junior in college"). In order to deal with structure, die parser's output needs to be "normalized" or "regularized" so diat complex terms with the same or closely related meanings would indeed receive matching representations. TTiis goal has been achieved to a certain extent in the present work. As it will be discussed in more detail below, indexiag terms were selected from among headmodifier pairs extracted from predicate-argument

123
representations of sentences.

Introduction of compound terms also complicates

the task of discovery of various semantic relaticmships

among them, including synonymy and subsmnption. For

example, the term natural language can be considered, in certain domains at least, to subsume any term denoting a

specific human language, such as English. Therefore, a query containing the former may be expected to retrieve documents containing the latter. The same can be said about language and English, unless language is in fact a

part of the compound term programming language in which case the association language - Fortran is appropriate. This is a problem because (a) it is a standard

practice to include both simple and compound terms in document representation, and (b) term associations have

thus far been computed primarily at word level (including fixed phrases) and therefore care must be taken when

such associations are used in term matching. This may

prove particularly troublesome for systems that attempt term clustering in order to create "meta-terms" to be used

in document representation.

The system presented here computes term associations from text at word and fixed phrase level and then uses these associations in query expansion. A fairly

primitive filter is employed to separate synonymy and

subsimiption relationships from others including anto- nymy and complementation, some of which are strongly

domain-dependent. This process has led to an increased

retrieval precision in experiments widi both ad-hoc and

routing queries for TREC-1 and TREC-2 experiments.

However, the actual improvement levels can vary substantially between different databases, types of runs (adhoc vs. routing), as well as the degree of prior processing

of the queries. We continue to study more advanced

clustering methods along with the changes in interpreta- tion of resulting associations, as signaled in the previous

paragraph.

In the remainder of this paper we discuss particulars of the present system and some of the observations

made while processing TREC-2 data. The above comments will provide the background for situating our

present effort and the state-of-the-art with respect to where we should be in the future.

OVERALL DESIGN

Our information retrieval system consists of a trad- itional statistical backbone (NIST's PRISE system; Har- man and Candela, 1989) augmented with various natural language processing components that assist the system in database processing (stemming, indexing, word and

phrase clustering, selectional restrictions), and translate a

user's information request into an effective query. This

design is a careful compromise between purely statistical non-lingmstic approaches and those requiring rather

accomplished (and expensive) semantic analysis of data, often referred to as 'conceptual retrieval'.

In our system the database text is first processed

with a fast syntactic parser. Subsequentiy certain types of

phrases are extracted from the parse trees and used as compound indexing terms in addition to single-word

terms. The extracted phrases are statistically analyzed as

syntactic contexts in order to discover a variety of similarity links between smaller subphrases and words occurring in them. A further filtering process maps these similarity links onto semantic relations (generahzation, specialization, synonymy, etc.) after which they are used to transform a user's request into a search query. The user's natural language request is also parsed,

and all mdexing terms occurring in it are identified. Certain highly ambiguous, usually single-word terms may be

dropped, provided that they also occur as elements in some compound terms. For example, "natural" is deleted from a query already containing "natural language"

because "natural" occurs in many unrelated contexts:

"natural nimiber", "natural logarithm", "natural approach", etc. At the same time, other terms may be

added, namely those which are linked to some query

term through admissible similarity relations. For example, "unlawful activity" is added to a query (TREC topic

055) containing the compound term "illegal activity" via a synonymy link between "illegal" and "unlawful". One of die striking observations made during the course of TREC-2 was to note diat removing low-quality terms from the queries is at least as important (and often more so) as adding synmyms and speciafizations. In some instances (e.g., routing runs) low-quality terms had

to be removed (or inhibited) before similar terms could

be added to the query or else the effect of query expansion was all but drowned out by the increased noise. ^

After the final query is constructed, the database

search follows, and a ranked Ust of documents is returned. It should be noted that all the processing steps, those performed by the backbone system, and those per- formed by the natural language processing components,

are fully automated, and no human intervention or manual encoding is required.

FAST PARSING WITH TTP PARSER

TTP (Tagged Text Parser) is based on the Linguistic String Grammar developed by Sager (1981). The

parser currentiy encompasses some 400 grammar productions, but it is by no means complete. The parser's

output is a regularized parse tree representation of each

' We would like to thank Donna Harman for turning our attention to the importance of term weighting schemes, including term deletion.

124
sentence, that is, a representation that reflects the sentence's logical predicate-argument structure. For

example, logical subject and logical object are identified in both passive and active sentences, and noun phrases

are organized aroimd their head elements. The parser is equipped with a powerful skip-and-fit recovery mechanism diat allows it to operate effectively in die face of ill- formed input or under a severe time pressiue. In die runs with approximately 130 million words of TREC's Wall

Street Journal and San Jose Mercury texts,^ the parser's

speed averaged between 0.3 and 0.5 seconds per sentence, or up to 70 words per second, on a Sun's SparcStation2. In addition, TTP has been shown to produce

parse structures which are no worse than those generated

by full-scale linguistic parsers when compared to handcoded Treebank parse trees. TTP is a full grammar parser, and initially, it attempts to generate a complete analysis for each sentence. However, unlike an ordinary parser, it has a built- in timer which regulates the amount of tune allowed for parsing any one sentence. If a parse is not returned before die allotted time elapses, die parser enters the

skip-and-fit mode in which it will try to "fit" die parse. While in the skip-and-fit mode, the parser will attempt to forcibly reduce incomplete constituents, possibly skipping portions of input in order to restart processing at a next imattempted constiment. In other words, the parser

will favor reduction to backtracking while in the skipand-fit mode. The result of this strategy is an approximate parse, partially fitted using top-down predictions.

The fragments skipped m the first pass are not thrown

out, instead they are analyzed by a simple phrasal parser

that looks for noun phrases and relative clauses and then

attaches die recovered material to the main parse structure. Full details of TTP parser have been described in the TREC-1 report (Strzalkowski, 1993a), as well as in other works (Strzalkowski, 1992; Strzalkowski &

Scheyen, 1993).

As may be expected, the skip-and-fit strategy will only be effective if the input skipping can be performed

with a degree of determinism. This means that most of

the lexical level ambiguity must be removed from the input text, prior to parsing. We achieve diis using a sto- chastic parts of speech tagger to preprocess die text (see

TREC-1 report for details). For TREC-2 a number of

problems have been corrected in the tagger, including

improper tokenization of input and handling of abbreviations.

WORD SUFFIX TRIMMER

Word stemming has been an effective way of

improving document recall since it reduces words to dieir common morphological root, dius allowing more successful matches. On die odier hand, stemming tends to decrease retrieval precision, if care is not taken to prevent situations where otherwise unrelated words are reduced to die same stem. In our system we replaced a

traditional morphological stemmer with a conservative

dictionary-assisted suffix trimmer. ^ The suffix trimmer

performs essentially two tasks: (1) it reduces inflected word forms to their root forms as specified in die dictionary, and (2) it converts nominaUzed verb forms (e.g., "implementation", "storage") to die root forms of

corresponding verbs (i.e., "implement", "store"). This is accomphshed by removing a standard suffix, e.g., "stor+age". replacing it widi a standard root ending

("+e"). and checking die newly created word against the

dictionary, i.e., we check whetiier die new root ("store")

is mdeed a legal word.

HEAD-MODIFIER STRUCTURES

Syntactic phrases extracted from TTP parse trees are head-modifier pahs. The head in such a pair is a central element of a phrase (main verb, main noim, etc.), while the modifier is one of the adjimct arguments of the head. In the TREC experiments reported here we

extracted head-modifier word and fixed-phrase pairs

only. While TREC databases are large enough to warrant

generation of larger compounds, we were in no position

to verify their effectiveness m indexmg. This was largely

because of die tight schedule, but also because of rapidly

escalating complexity of the indexing process: even witii 2-word phrases, compound terms accounted for nearly

88% of all index entries, m other words, including 2- word phrases mcreased die index size approximately 8

times. Let us consider a specific example from the WSJ

database: The former Soviet president has been a local hero

ever since a Russian tank invaded Wisconsin. The tagged sentence is given below, followed by the reg- ularized parse structure generated by TTP, given in Fig- ure 1.

Tht/dt former/y Soviet/yy president/nw has/vbz he&n/vbn sjdt local//; hero/«« ever/rb since/m n/dt Russian/y tank/«n invaded/vM Wisconsin/np ./per

' Dealing with prefixes is a more complicated matter, since they may have quite strong effect upon the meaning of the resulting term,

^ Approximately 0.85 GBytes of text, over 6 million sentences. e.g., un- usually introduces explicit negation.

125
[assert [[perf[HAVE]]

[[verb [BE]]

[subject [np

[n PRESIDENT]

[t_posTHE]

[adj [FORMER]]

[adj [SOVIET]]]]

[object [np

[nHERO]

[t_pos A]

[adj [LOCAL]]]]

[adv EVER]

[sub_ord

[SINCE

[[verb [INVADE]]

[subject [np

[n TANK]

[t_pos A]

[adj [RUSSL\N]]]]

[object [np[name [WISCONSIN]]]]]]]]]]

Figure 1. Predicate-argument parse structure.

It should be noted that the parser's output is a predicate-argument structvu-e centered around main ele- ments of various phrases. Li Figure 1, BE is the main

predicate (modified by HAVE) with 2 arguments (subject, object) and 2 adjuncts (adv, sub ord). INVADE is the predicate in the subordinate clause with 2 argmnents

{subject, object). The subject of BE is a noun phrase

with PRESIDENT as the head element, two modifiers

(FORMER, SOVIET) and a determiner (THE). From this structure, we extract head-modifier pairs that become

candidates for compound terms. The following types of

pairs are considered: (1) a head noun and its left adjective or noun adjimct, (2) a head noun and the head of its right adjunct, (3) the main verb of a clause and the head

of its object phrase, and (4) the head of the subject

phrase and the main verb. These types of pairs account

for most of the syntactic variants for relatmg two words

(or simple phrases) into pairs carrying compatible

semantic content. For example, the pair

retrieve+ information will be extracted from any of the following fragments: information retrieval system;

retrieval of information from databases; and information

that can be retrieved by a user-controlled interactive search process. In the example at hand, die following

head-modifier pairs are extracted (pairs containing lowcontent elements, such as BE and FORMER, or names.

such as WISCONSIN, will be later discarded):

PRESIDENT+BE, PRESIDENT+FORMER, PRESIDENT+SOVEET,

BE+HERO, HERO+LOCAL,

TANK+INVADE. TANK+RUSSL\N, INVADE+WISCONSIN

We may note that the three-word phrase former Soviet

president has been broken into two pairs former

president and Soviet president, both of which denote

things that are potentially quite different from what the origmal phrase refers to, and this fact may have potentially negative effect on retrieval precision. This is one

place where a longer phrase appears more appropriate.

The representation of this sentence may therefore contain

the following terms (along with their inverted document

frequency weights):

PRESIDENT 2.623519 SOVIET 5.416102 PRESIDENT+SOVDET 11.556747 PRESIDENT+FORMER 14.594883 HERO 7.896426 HERO+LOCAL 14.314775 INVADE 8.435012 TANK 6.848128 TANK+INVADE 17.402237 TANK+RUSSIAN 16.030809 RUSSL«iN 7.383342 WISCONSIN 7.785689 While generating compound terms we took care to identify 'negative' terms, that is, those whose denotations

have been explicitly excluded by negation. Even though

matching of negative terms was not used in retrieval (nor

did we use negative weights), we could easily prevent

matching a negative term in a query against its positive

counterpart in the database by removing known negative terms from queries. As an example consider the following fragment from topic 067:

It should NOT be about economically-motivated

civil disturbances and NOT be about a civil distur- bance directed against a second country. The corresponding compound terms are: NOT disturb+civil NOT country+second NOT direct+disturb

The particular way of interpreting syntactic contexts was dictated, to some degree at least, by statistical considerations. Our initial experiments were performed on a relatively small collection (CACM-3204), and there- fore we combined pairs obtained from different syntactic

relations (e.g., verb-object, subject-verb, noim-adjunct.

etc.) in order to increase frequencies of some associations. This became largely unnecessary in a large collection such as TIPSTER, but we had no means to test alter- native options, and thus decided to stay with the original.

It should not be difficult to see that this was a compromise solution, since many important distinctions were

125
potentially lost, and strong associations could be pro- duced where there weren't any. A way to improve things

is to consider different syntactic relations independently,

perhaps as independent sources of evidence that could

lend support (or not) to certain term similarity predictions. We have started investigating this option during

TREC-2, however, it has not been sufficiently tested yet. One difficulty in obtaining head-modifier pairs of

highest accuracy is the notorious ambiguity of nominal

compounds. For example, the phrase natural language

processing should generate language^natural and

processing+language, while dynamic information processing is expected to yield processing+dynamic and

processing+information. A still another case is executive

vice president where the association president+executive

may be stretching things a bit too far. Since our parser has no knowledge about the text domain, and uses no

semantic preferences, it does not attempt to guess any

internal associations within such phrases. Instead, this task is passed to the pair extractor module which

processes ambiguous parse structures in two phases. In phase one, all and only unambiguous head-modifier pairs are extracted, and the frequencies of their occurrences

are recorded. In phase two, frequency information about

pairs generated in the first pass is used to form associations firom ambiguous shnctures. For example, if language+natural has occurred imambiguously a niunber of times in contexts such as parser for natural language, while processing +natural has occurred

significantiy fewer times or perhaps none at all, then we

will prefer the former association as vaUd. In TREC-2

phrase disambiguation was not used, instead we decided

to avoid ambiguous phrases altogether. While our disambig program worked generally satisfactorily, we could

resolve only a small fraction of cases (about 7%) and

thus it's impact on the overall system's performance was

limited. However, query-level disambiguation may be more important.

TERM CORRELATIONS FROM TEXT

Head-modifier pairs form compound tenns used in database indexing. They also serve as occurrence contexts for smaller terms, including single-word terms. If two terms tend to be modified with a number of common

modifiers and otherwise appear in few distinct contexts, we assign them a similarity coefficient, a real number

between 0 and 1. The similarity is determined by comparing distribution characteristics for botii terms within

tiie corpus: how much information content do they carry, do their information contribution over contexts vary

gready, are the common contexts in which these terms occur specific enough? In general we will credit high- content terms appearing in identical contexts, especially

if these contexts are not too commonplace.'* For TREC-2 runs we used a similarity fcffmula which, unlike Uie similarity formula used in TREC-1,

produces clusters of related words and phrases, but will not generate imiform term similarity ranking across clusters. This new formula, however, appeared better suited to handle the diverse subject matter of the WSJ database. We used a (revised) variant of weighted Tanimoto's measure described in (Grrefenstette, 1992):

with

Y,MlNW{\.x,att]),W(\y,att^)

alt £M/lA:(iy([;c,a«]).M/([y,a/^])

alt

W{[x,y'i) = GEW{x)*log{f,^y)

GEW{x) = l + ^

^*log Jx,y n

y J logiN)

In the above, f^y stands for absolute frequency of pair

[•^.>' ]. ^ is the frequency of term y, and is the number

of single-word terms. Sample clusters obtaiued from

approx. 250 MByte (42 million words) subset of WSJ

(years 1990-1992) are given in Table 1.

In order to generate better similarities, we require

tiiat words Xi and X2 appear in at least M distinct com- mon contexts, where a common context is a couple of

pairs [xi ,y] and [x2,y], or [y,Xi] and [yjC2] such that they

each occurred at least three times. Thus, banana and Baltic will not be considered for similarity relation on die basis of their occurrences in the common context of

republic, no matter how frequent, unless there is another

such common context comparably frequent (there wasn't any in TREC's WSJ database). For smaller or narrow domain databases Af=2 is usually sufficient. For large databases covering a rather diverse subject matter, like WSJ or SJMN (San Jose Mercury News), we used M^5.^

This, however, turned out not to be sufficient. We would

still generate fairly strong similarity links between terms such as aerospace and pharmaceutical where 6 and more common contexts were found. In the example at hand the following common contexts were located, all occurring

at the head (left) position of a pair (at right are their glo- bal entropy weights and frequencies with aerospace and

* It would not be appropriate to predict similarity between

language and logarithm on the basis of their co-occurrence with rnlural.

' For example banana and Dominican were found to have two common contexts: republic and plant, although this second occurred in apparently different senses in Dominican plant and banana plant.
pharmaceutical, respectively):

finn

industry

sector concern

analyst

division

giant

GEW=0.58

GEW=0.51

GEW=0.61

GEW=0.50

GEW=0.62

GEW=0.53

GEW=0.62

fxly=9 fx2y=22

fxly=84 fx2y=56

fxly=5 fx2y=9

fxly=130 fx2y=115

fxly=23 fx2y=8

fxly=36 fx2y=28

fxly=15 fx2y=12

Note that while some of these weights are qxiite low (less than 0.6 ~ GEW takes values between 0 and 1), thus

indicating a low importance context, the frequencies with

which these contexts occurred with both terms were high

and balanced on both sides (e.g., concern), thus adding to the strength of association. We are now considering additional thresholds to bar low importance contexts from

being used in similarity calculation.

It may be worth pointing out tiiat the similarities are calculated using term co-occurrences in syntactic rather than in document-size contexts, the latter being the usual practice ia non-linguistic clustering (e.g., Sparck

Jones and Barber, 1971; Crouch, 1988; Lewis and Goft,

1990). Although the two methods of term clustering may

be considered mutually complementary in certain situations, we beheve that more and stronger associations can

be obtained through syntactic-context clustering, given

sufficient amount of data and a reasonably accurate syntactic parser.'

QUERY EXPANSION

Similarity relations are used to expand user queries

with new terms, in an attempt to make the final search query more comprehensive (adding synonyms) and/or more pointed (adding specializations).^ It follows that not

all similarity relations will be equally useful in query

expansion, for instance, complementary and antonymous

relations like the one between Australian and Canadian,

accept and reject, or even generalizations like from

* Other common contexts, such as company or market, have al- ready been rejected because they were paired with too many different words (a high dispersion ratio, see note 12).

' Non-syntactic contexts cross sentence boundaries with no fuss, which is helpfiil with short, succinct documents (such as CACM

abstracts), but less so with longer texts; see also (Grishman et al., 1986).

' Query expansion (in the sense considered here, though not quite

in the same way) has been used in information retrieval research before

(e.g., Sparck Jones and Tait, 1984; Harman, 1988), usually with mixed

results. An alternative is to use term clusters to create new terms, "metaterms", and use them to index the database instead (e.g.. Crouch, 1988; Lewis and Croft, 1990). We found that the query expansion approach

gives the system more flexibility, for instance, by making room for hypertext-style topic exploration via user feedback.

aerospace to industry may actually harm system's per- formance, since we may end up retrieving many

irrelevant documents. On the otiier hand, database search

is likely to miss relevant documents rf we overlook the

fact that vice director can also be deputy director, or that takeover can also be merge, buy-out, or acquisition. We

noted that an average set of similarities generated from a

text corpus contains about as many "good" relations (synonymy, specialization) as "bad" relations (antonymy,

complementation, generalization), as seen from the query

expansion viewpoint. Therefore any attempt to separate

diese two classes and to increase the proportion of

"good" relations should result in improved retrieval. This

has indeed been confirmed in our experiments where a

relatively crude filter has visibly increased retrieval precision.

In order to create an appropriate filter, we devised

a global term specificity measure (GTS) which is calculated for each term across all contexts in which it occurs. The general philosophy here is that a more specific

word/phrase would have a more limited use, i.e., a more

specific term would appear in fewer distinct contexts. In

this respect, GTS is similar to the standard inverted documentfrequency (idf) measure except that term frequency

is measured over syntactic units rather than document

size units.^ Terms with higher GTS values are generally

considered more specific, but the specificity comparison

is only meaningful for terms which are already known to be sunilar. The new function is calculated according to the following fonniila:

GTS(w)=-{

ICiiw) * ICr(w) if both exist ICniw) ii only ICfiiw) exists ICiiw) otherwise

where (with n^^. dy^, > 0):

/Q(w) = /C([h;,J) =

/C«(m;)=/C(L,>v]) =

d^(n^+d^-l)

d^(n^+d^-l)

For any two terms Wi and W2, and a constant 6 > 1, if GTS(w2)tb* GTS(wi) then is considered more

specific than Wi. In addition, if SIM^rmi^i'^i) = o> Q, where 0 is an empirically established threshold, then W2

can be added to the query containing term Wj with

weight cf}^ For example, the following were obtained

' We believe that measuring term specificity over document-size

contexts (e.g., Sparck Jones, 1972) may not be appropriate in this case. In particular, syntax-based contexts allow for processing texts without any internal document structure. For TREC-2 we used a = 0.2; 6 varied between 10 and 100.

128
from the WSJ training database: GTS {takeover) = 0.00145576

GTS {merge) = 0.00094518

GTS {buy -out) = 0.00272580

GTS {acquire ) = 0.00057906

with

SIM {takeover,merge) = 0. 190444

SIM {takeover, buy -out) = 0. 157410

SIM {takeover,acquire ) = 0. 1 39497

SIM {merge, buy -out) = 0. 133800

SIM {merge, acquire) =0.263772

SIM {buy -out,acquire) = 0. 109106

Therefore both takeover and buy-out can be used to specialize merge or acquire. With this filter, the relation- ships between takeover and buy-out and between merge

and acquire are either both discarded or accepted as synonymous. At this time we are unable to tell synonymous or near synonymous relationships from

those which are primarily complementary, e.g., man and woman.

In. TREC-1 the impact of query expansion through term similarities on the system's overall performance was generally disappointing. For TREC-2 we have made

a number of changes to the term correlation model, but

again time limitations prevented us from properly testing

all options. Among the most important changes are:

(1) Exclusion of pairs obtained from SUBJECTVERB relations: we determined diat these contexts are generally of little use as neither subject nor verb subcategorizes well for the other. Moreover we observed that the presence of these pairs was the source of many unwanted term associations.^^

(2) Automatic pruning of low-content terms from the

queries: terms with low idf weights, terms with

low information contribution weights that are elements of compound terms, are removed from

queries before database search. As we tuned

various cutoff thresholds we noted that a

significant increase in both recall and precision

could be obtained.

" Subject-Verb pairs were retained as compound terms, however. The Information Contribution measure indicates the strength of word pairings, and is defined as IC(x, [x,y])= where is

the absolute frequency of pair [x,y] in the corpus, rij, is the frequency of term x at the head position, and dj^ is a dispersion parameter imderstood

as the number of distinct syntactic contexts in which term x is found.

word cluster

takeover merge, buy-out, acquire, bid

benefit compensate, aid, expense

capital cash.fiind, money

staff personnel, employee,force

attract lure, draw, woo

sensitive crucial, difficult, critical

speculate rumor, uncertainty, tension

president director, executive, chairman

vice deputy

outlook forecast, prospect, trend

law rule, policy, legislate, bill

earnings profit, revenue, income

portfolio asset, invest, loan

inflate growth, demand, earnings

industry business, company, market

growth increase, rise, gain

firm bank, concern, group, unit

environ climate, condition, situation

debt loan, secure, bond

lawyer attorney

counsel attorney, administrator, secretary

compute machine, software, equipment

competitor rival, competition, buyer

alliance partnership, venture, consortium

big large, major, huge, significant

fight battle, attack, war, challenge

base facile, source, reserve, support

shareholder creditor, customer, client

investor, stockholder

Table 1. Selected clusters obtained from syntactic contexts, derived from approx. 40 million words of WSJ text, with weighted Tanimoto

formula.

I 129
CREATING AN INDEX

Most of the problems we encountered when adapting NIST's PRISE system for use in TREC-2 had to do

with the size of the data that had to be indexed.

We had to deal with the restrictions imposed by the resources we had (e.g., only 96 MBytes of virtual memory). The rest of this paragraph signals some of the changes we made to the NIST system in order to deal with our restrictions. The original system would request

twice the previously requested amount of memory each

time it needed more. As a result of this the system would

reach the limit of virtual memory after only a relatively

small portion of the total number of docimients had been

indexed. In our version, the memory requested by the system grows linearly. The increments are estimated in such a way that the system never requests too much

memory.

The indexing process became too fragile when the

limits of the environment were approached. When a

large portion of the virtual memory and of the disk space was being used by the indexing process, crashes became

very likely. Unfortunately, it turned out that the process was very difficult to restart after some crashes (e.g., in the rebuild phase), thus leading to time consiuning

repeats.

Indexing also takes too long at present. Given the

size of the data to be indexed the whole process takes at least 250 hours if everything goes well, which happens

seldom. Given TREC-2's deadlines we could not afford

to perform too many experiments: we barely had time to index the corpus once. Most of the previous problems could be solved by

distributing the indexing process to several different machines and performmg the mdexing m paraUel.

We beheve that it is possible to create several small indexes instead of a single very large one. If certain rules are followed when creating the distributed index, it should be possible to merge the results of querying the set of small indexes and to obtain a performance

(recall and precision) comparable to the results obtained

using a single index. The test setup we built in order to perform the experiments required for TREC-2 should

allow us to test these hypotheses. The advantages of a

distributed index are clear:

(1) The indexing process would be faster.

(2) Each one of the distributed indexing processes would be smaller and less fragile.

(3) Even if one of the distributed processes crashes

restarting it would be less expensive.

(4) A distributed system would be much easier to update, i.e., adding a new document would not

require to reindex the whole corpus.

(5) A distributed system would be more likely to be

useful in order to study the kinds of problems and

soluti^ons that are likely to be encountered in a

real world situation.

SUMMARY OF RESULTS

We have processed the total of 850 MBytes of text during TREC-2. The first 550 MBytes were articles from

the Wall Street Journal which were previously processed

for TREC-1; we had to repeat most of the processing to correct early tokenization errors introduced by the

tagger. The entire process (tagging, parsing, phrase

extraction) took just over 4 weeks on 2 Sun's SparcStations (1 and 2). Building a comprehensive index for the WSJ database took up another 2 weeks. This time we

were able to create a single index thanks to the improved

indexing software received from NIST. The final index

size was 204 MBytes, and included 2,274,775 unique

terms (of which a1x)ut 310,000 were single word terms, and the remaining 1,865,000 were syntactic word pairs)

occurring in 173,219 documents, or more than 13

(unique) terms per document. Note that this gives potentially much better coverage of document content than

smgle word terms alone with less than 2 unique terms per document. We say 'potentially' since the process of

deriving phrase-level terms from text is still only partially understood, including the complex problem of

'normalization' of representation.

The remaining 300 MBytes were articles from the San Jose Mercury News, which were contained in TIP- STER disk-3. Processing of this part, and creating an

index for routing purposes took about 3 weeks. While

natural language processmg required 2 weeks to complete (at approximately the same speed as WSJ database), we were able to cut indexing time in half by using

a faster in-memory version of the NIST system. This new

version reduces the time required by the first phase of

indexing from days to hours, however the second phase

remains slow (days) and fragile (we had to redo it 3

times). The final size of the SJMN index was 101

MBytes, with 1,535,971 unique terms occurring in 86,270 documents (nearly 18 unique- terms per document).'^

Two types of retrieval have been done: (1) new

topics 101-150 were run in the ad-hoc mode against WSJ

database, and (2) topics 51-100, previously used in TElEC-1, were run in die routing mode against SJMN

database. In each category several runs were attempted

^ It has to be noted that the ratios at which new terms are generated are nearly identical in both databases; at 86,319 documents (or about half way through WSJ database) 1,335,622 unique terms had

been recorded.

130
with a different combination of fields from the topics

used to create search queries. A typical topic is shown

below:

<top> <head> Tipster Topic Description <num> Number: 107 <doin> Domain: International Economics

<title> Topic: Japanese Regulation of Insider Trading <desc> Description: Document will inform on Japan's regulation of insider

trading. <narr> Narrative: A relevant document will provide data on Japanese laws,

regulations, and/or practices which help the foreigner understand how Japan controls, or does not control, stock market practices which could be labeled as insider

trading. <con> Concept(s):

1. insider trading

2. Japan

3. Ministry of Finance, Securities and Exchange Council, Osaka Securities Exchange, Tokyo Stock Exchange

4. Securities and Exchange Law, Article 58, law,

legislation, guidelines, self-regulation

5. Nikko Securities, Yamaichi Securities, Nomura Securities, Daiwa Securities, Big Four brokerage firms <fac> Factor(s): <nat> Nationality: Japan

</fac> <Aop>

This topic actually consists of two different statements of

the same query: the natural language specification consisting of <desc> and <narr> fields, and an expert- selected list of key terms which are often far more infor- mative than the narrative part (in some cases these terms were selected via feedback in actual retrieval attempts).

The table below shows the search query obtained from

fields <desc> and <narr> of topic 107, after an expansion

with similar terms and deleting low-content terms.

Query 107

standard+trade idf=16.81 weight=0.38

standard+trade idf=16.81 weight=0.38

regulate+japanese idf=15.40 weight= 1.00 standard+japanese idf=14.08 weight=0.38

regulate+trade idf=12.84 weight= 1.00 regulate+trade idf=12.84 weight= 1.00

controls idf=9.97 weight= 1.00

labele idf=9.20 weight= 1.00 trade+inside idf=8.62 weight= 1.00 trade+inside idf=8.62 weight= 1.00

inside idf=6.49 weight= 1.00

inside idf=6.49 weight= 1.00

inside idf=6.49 weight= 1.00

regulate idf=5.66 weight= 1.00 regulate idf=5.66 weight= 1.00

regulate idf=5.66 weight=1.00

practice idf=5.46 weight=1.00

practice idf=5.46 wcight=1.00

data idf=4.91 weight=1.00

data idf=4.91 weight:4).51 data idf=4.91 weight=0.26

Japanese idf=4.84 weight=1.00

Japanese idf=4.84 weight=1.00

standard idf=4.81 weighl=0.38

standard idf=4.81 weight=0.38

standard idf=4.81 weight=0.38

inform idf=4.71 weight=1.00

inform idf=4.71 weight=0.26

inform idf=4.71 weight=0.51

protect idf=4.69 weight=0.41

Note that many 'function' words have been removed

from the query, e.g., provide, understand, as well as other 'common words' such as document and relevant

(this is in addition to our regular list of 'stopwords').

Some stUl remain, however, e.g., data and inform,

because these could not be uniformly considered as 'common' across all queries.

Results obtained for queries using text fields only

and those based primarily on keyword fields are reported

separately. The purpose of this distinction was to demonstrate that (or whether) an intensive natural language pro- cessing can make an imprecise and fiiequently convoluted narrative into a better query that an expert would

create.^'* The ad-hoc category runs were done as follows

(these are the official TREC-2 results):

(1) nyuirl: An automatic run of topics 101-150

against the WSJ database with the following

fields used: <tide>, <desc>, and <narr> only.

Both syntactic phrases and term similarities were

included.

(2) nyuirl: An automatic run of topics 101-150

against die WSJ database with the following

fields used: <tide>, <desc>, <con> and <fac>

only. Both syntactic phrases and term similarities were included.

Some results on the Impact of different fields in TREC topics on the final recall/precision results were reported by Broglio and Croft (1993) at the ARPA HLT workshop, although text-only runs were not

included. One of the most striking observations they have made is that the narrative field is entirely disposable, and moreover that its inclusion

in the query actually hurts the system's performance. Croft (personal communication, 1992) has suggested that excluding aU expert-made

fields (i.e., <con> and <fac>) would make the queries quite ineffective. Broglio (personal communication, 1993) confirms this showing that text-only retrieval (i.e., with <desc> and <narr>) shows an average pre- cision at more than 30% below that of <con>based retrieval.

131
(3) nyuirS: A run of manually pruned tq)ics 101-150

against the WSJ database with the following

fields used: <title>, <desc>, <con> and <fac>

only. Both syntactic phrases and term similarities were included. Manual intervention involved

removing some terms from queries before data- base search. Summary statistics for these runs are shown in Table 2. In addition, the 'base' colimm reports the system's performance on text fields widi no language

preprocessiag, and no phrase terms or similarities used. We must note, however, that in all cases the topics have

been processed with our suffix-trimmer, which means some NLP has been done already (tagging + lexicon),

and therefore what we do not ^ee here a performance of

'pure' statistical system.

In the routing category only automatic runs were

done (again, these are the official TREC-2 results):

(1) nyuirl: An automatic run of topics 51-100

against the SJMN database with the following

fields used: <titie>. <desc>, and <narr> only.

Both syntactic phrases and term similarities were

included.

(2) nyuirl: An automatic run of topics 51-100

against the SJMN database with the followmg

fields used: <title>, <desc>, <con> and <fac>

oidy. Both syntactic phrases and term similarities were included.

A (simulated) routing mode run means that queries

(i.e., terms and their weights) were derived with respect

to a (different) training database (here WSJ), and were

subsequendy run against the new database (here SJMN).

In particular, this means that the terms and their relative importance (reflected primarily through idf weights) were those of WSJ database rather than SJMN database. Routing runs are siunmarized in Table 3. Again a column 'base' is added to show the system's perfor- mance without NLP module. We may note that the rout- ing results are generally well below the ad-hoc results, both because the base system performance is inferior and

because query processing has a different effect on the

final statistics. The last colimm is a post-TREC run.^^

It should be noted that in category B runs, three topics (63, 65, and 88) had no relevant documents in SJMN database. Unfortunately,

the evaluation program counts those as if there were relevant documents

but none had been found, thus underestimating the system's perfor- mance by 5 to 8%. Excluding these three topics from consideration we

obtain, in the last column, the average precision of 0.2624 and the Rprecision of 0.3000.

Run base nyuirl nyuir2 nyuir3 Name ad-hoc ad-hoc ad-hoc ad-hoc

Queries 50 50 50 50

Tot number of docs over aU queries

Ret 49887 49884 49876 49877

Rel 3929 3929 3929 3929

RelRet 2740 2983 3274 3281

Recall (interp) Precision Averages

0.00 0.7038 0.7013 0.7528 0.7528

0.10 0.4531 0.4874 0.5567 0.5574

0.20 0.3708 0.4326 0.4721 0.4724

0.30 0.3028 0.3531 0.4060 0.4076

0.40 0.2550 0.3076 0.3617 0.3621

0.50 0.2059 0.2637 0.3135 0.3142

0.60 0.1641 0.2175 0.2703 0.2711

0.70 0.1180 0.1617 0.2231 0.2237

0.80 0.0766 0.1176 0.1667 0.1697

0.90 0.0417 0.0684 0.0915 0.0916

1.00 0.0085 0.0102 0.0154 0.0160

Average precision over aU rel docs

Avg 0.2224 0.2649 0.3111 0.3118

Precision at

5 docs 0.4640 0.4920 0.5360 0.5360

10 docs 0.4140 0.4420 0.4880 0.4880

IS docs 0.3867 0.4240 0.4693 0.4707 20 docs 0.3670 0.4050 0.4390 0.4410 30 docs 0.3253 0.3640 0.4067 0.4080

100 docs 0.2304 0.2720 0.3094 0.3094 200 docs 0.1626 0.1886 0.2139 0.2140 500 docs 0.091

1

0.1026 0.1137 0.1140 1000 docs 0.0548 0.0597 0.0655 0.0656

R-Precision (after RelRet)

Exact 0.2605 0.3003 0.3320 0.3321

Table 2. Automatic ad-hoc run statistics for queries 101-150 against WSJ database: (1) base - statistical terms only with <desc> and <narr>

fields; (2) nyuirl - using syntactic phrases and similarities with <desc>

and <narr> fields only; (3) nyuirl - same as 2 but with <desc>, <con>,

and <fac> fields only; and (4) nyuir3 - same as 3 but queries manually

pruned before search.

132
Run base nyuirl nyuir2 nyuir2a

Name routing routing routing routing

Queries 50 50 50 50

Tot number of docs over all queries

Ret 50000 50000 50000 50000

Rel 2064 2064 2064 2064

RelRet 1349 1390 1610 1623

Recall (interp) Precision Averages

0.00 0.5276 0.5400 0.6435 0.6458

0.10 0.3685 0.3937 0.4610 0.5021

0.20 0.3054 0.3423 0.3705 0.4151

0.30 0.2373 0.2572 0.3031 0.3185

0.40 0.2039 0.2263 0.2637 0.2720

0.50 0.1824 0.2032 0.2282 0.2379

0.60 0.1596 0.1674 0.1934 0.1899

0.70 0.1167 0.1295 0.1542 0.1571

0.80 0.0854 0.0905 0.1002 0.1163

0.90 0.0368 0.0442 0.0456 0.0434

1.00 0.0228 0.0284 0.0186 0.0158

Average precision over all rel docs

Avg 0.1884 0.2038 0.2337 0.2466

Precision at

5 docs 0.3160 0.3360 0.4280 0.4440

10 docs 0.3100 0.3240 0.4000 0.4180

IS docs 0.2813 0.2933 0.3613 0.3800 20 docs 0.2670 0.2790 0.3260 0.3530 30 docs 0.2240 0.2404 0.2760 0.2993 100 docs 0.1306 0.1412 0.1708 0.1698 200 docs 0.0865 0.0939 0.1078 0.1107 500 docs 0.0464 0.0489 0.0575 0.0570 1000 docs 0.0270 0.0278 0.0322 0.0325

R-Precision (after Rel)

Exact 0.2196 0.2267 0.2513 0.2820

Table 3. Automatic routing run statistics for queries 51-100 against SJMN database: (1) base - statistical terms only with <desc> and

<narr> fields; (2) rtyuir] - using syntactic phrases and similarities with <desc> and <narr> fields only; (3) nyuir2 - same as 2 but with <desc>, <con>, and <fac> fields only; and (4) nyuir2a - run nyuirl repeated with new weighting for phrases. TERM WEIGHTING ISSUES

Finding a proper term weighting scheme is critical in tmn-based retrieval since the rank of a docimxent is

determined by the weights of the terms it shares with the query. One popular term weighting scheme, known as

tf.idf, weights terms proportionately to their inverted

document frequency scores and to their in-document fre- quencies (tf). The in-document frequency factor is asually normalized by the document length, that is, it is more significant for a term to occur 5 times in a short 20-word document, than to occur 10 times in a 1000-

word article.

In our official TREC runs we used the normalized

tf.idf weights for all terms alike: single 'ordinary-word'

terms, proper names, as well as phrasal terms consisting

of 2 or more words. Whenever phrases were included in the term set of a document, the length of this document was increased accordingly. This had die effect of

decreasing tf factors for 'regular' smgle word terms. A standard tf.idf weighting scheme (and we

suspect any other uniform scheme based on frequencies)

is inappropriate for mixed term sets (ordinary concepts, proper names, phrases) because:

(1) It favors terms that occiu' fairly fiequendy in a dociunent, which supports only general-type

queries (e.g., "all you know about 'star wars'").

Such queries are not typical in TREC.

(2) It attaches low weights to infrequent, highly

specific terms, such as names and phrases, whose

only occurrences in a document often decide of

relevance. Note that such terms cannot be reU- ably distinguished using their distribution in the database as the sole factor, and therefore syntactic and lexical information is required.

(3) It does not address the problem of mter-term dependencies arising when phrasal terms and

their component single-word terms are all included in a document representation, i.e., launch-^satellite and satellite are not independent, and it is tmclear whether diey should be

counted as two terms.

In our post-TREC-2 experiments we considered

(1) and (2) only. We changed die weighting scheme so

that the phrases (but not die names which we did not dis- tinguish in TREC-2) were more heavily weighted by

their idf scores while the in-dociunent frequency scores were replaced by logarithms multiplied by sufficiendy

large constants, hi addition, the top N highest-idf matchmg terms (simple or compound) were counted more

toward die document score dian die remaining terms. This 'hot-spot' retrieval option is discussed in die next

section.

'* This is not always true, for example when all occurrences of a term are concentrated in a single section or a paragraph rather than

spread around the article. See the following section for more discussion.

133
The table below illustrates the problem of weightmg phrasal terms Using topic 101 and a relevant document (WSJ870226-0091).

Topic 101 matches WSJ870226-0091

duplicate terms not shown TERM TFJDF NEW WEIGHT

sdi 1750 1750

eris 3175 3175

star 1072 1072

wars 1670 1670

laser 1456 1456 weapon 1639 1639

missile 872 872

space+base 2641 2105

interceptor 2075 2075

exoatmospheric 1879 3480

system+defense 2846 2219

reentry+vehicle 1879 3480

initiative+defense 1646 2032

system+interceptor 2526 3118 DOC RANK 30 10 Changing the weighting scheme for compound terms, along with other minor improvements (such as expanding

the stopword list for topics, or correcting a few parsing

bugs) has lead to the overall increase of precision of

nearly 20% over our official TREC-2 ad-hoc results. Table 4 summarizes these new runs for queries 101-150

against WSJ database. Similar improvements have been

obtained for queries 51-100.

The results of the routing runs against SJMN data- base are somewhat more troubling. Applying the new

weighting scheme we did see the average precision

increase by some 5 to 12% (see column 4 in Table 3), but

the results remain far below those for the ad-hoc nins. Direct runs of queries 51-100 against SJMN database

produce results that are about the same as in the routing runs (which may indicate that our routing scheme works

fine), however the same queries run against WSJ data- base have retrieval precision some 25% above SJMN

runs. This may indicate some problems with SJMN data- base or the relevance judgements for it.

HOT SPOT' RETRIEVAL

Another difficulty with frequency-based term

weighting arises when a long document needs to be

retrieved on the basis of a few short relevant passages. If the bulk of the document is not direcdy relevant to the query, then there is a strong possibiUty that the document

will score low in the final rankmg, despite some strongly relevant material in it. This problem can be dealt with by

subdividing long documents at paragraph breaks, or into approximately equal length fragments and indexing the database with respect to these (e.g., Kwok 1993). While

Run nyuirl nyuirl a nyuir2 nyuir2a Name ad-hoc ad-hoc ad-hoc ad-hoc

Queries 50 50 50 50

Tot number of docs over aU queries

Ret 49884 50000 49876 50000

Rel 3929 3929 3929 3929

RelRet 2983 3108 3274 3401

Recall (interp) Precision Averages

0.00 0.7013 0.7201 0.7528 0.8063

0.10 0.4874 0.5239 0.5567 0.6198

0.20 0.4326 0.4751 0.4721 0.5566

0.30 0.3531 0.4122 0.4060 0.4786

0.40 0.3076 0.3541 0.3617 0.4257

0.50 0.2637 0.3126 0.3135 0.3828

0.60 0.2175 0.2752 0.2703 0.3380

0.70 0.1617 0.2142 0.2231 0.2817

0.80 0.1176 0.1605 0.1667 0.2164

0.90 0.0684 0.1014 0.0915 0.1471

1.00 0.0102 0.0194 0.0154 0.0474

Average precision over all rel docs

Avg 0.2649 0.3070 0.3111 0.3759

Precision at

5 docs 0.4920 0.5200 0.5360 0.6040

10 docs 0.4420 0.4900 0.4880 0.5580

15 docs 0.4240 0.4653 0.4693 0.5253 20 docs 0.4050 0.4420 0.4390 0.4980 30 docs 0.3640 0.3993 0.4067 0.4607

100 docs 0.2720 0.2914 0.3094 0.3346 200 docs 0.1886 0.2064 0.2139 0.2325 500 docs 0.1026 0.1103 0.1137 0.1229 1000 docs 0.0597 0.0622 0.0655 0.0680

R'Precision (after Rel)

Exact 0.3003 0.3332 0.3320 0.3950

Table 4. Automatic ad-hoc run statistics for queries 101-150 against WSJ database: (1) nyuirl - TREC-2 official run with <desc> and <narr>

fields only; (2) nyuirla - revised term weighting run; (3) nyuirl -

official TREC-2 run with <desc>, <con>, and <fac> fields only; and (4) nyuirla - revised weighting run.

such approaches are effective, they also tend to be costly

because of increased index size and more complicated

134
access methods.

Efficiency considerations has led us to investigate an alternative approach to the hot spot retrieval which

would not require re-indexing of the existing database or any changes in document access. In our approach, the maximum number of terms on which a query is permitted

to match a document is lunited to N highest weight

terms, where N can be the same for all queries of may

vary from one query to another. Note that diis is not the same as simply taking the N top terms from each query. Rather, for each document for which diere are M matching terms with the query, only min(M,N) of them,

namely those which have highest weights, will be considered when computing the document score. Moreover,

only the global importance weights for terms are considered (such as idf), while local in-document frequency

(eg., tO is suppressed by either taking a log or replacing

it with a constant. The effect of this 'hot spot' retrieval is shown below in the ranking of relevant documents within

the top ICXX) retrieved documents for topic 65:

Full tf.idf retrieval DOCUMENT ID RANK SCORE

WSJ870304-0091 4 12228 WSJ891017-0156 7 9771 WSJ920226-0034 14 8921 WSJ870429-0078 26 7570 WSJ870205-0078 33 6972 WSJ8807 12-0033 34 6834 WSJ9201 16-0002 37 6580 WSJ910328-0013 74 4872 WSJ910830-0140 80 4701 WSJ890804-0138 102 4134 WSJ91 1212-0022 104 4065 WSJ870825-0026 113 3922 WSJ880712-0023 135 3654 WSJ871202-0145 153 3519

Hot-spot idf-dominated with N=20

DOCUMENT ID RANK SCORE

WSJ920226-0034 1 11955 WSJ8703O4-0091 3 11565 WSJ870429-0078 5 9997 WSJ9201 16-0002 7 9997 WSJ910830-0140 11 8792 WSJ870205-0078 20 8402 WSJ910328-0013 29 8402 WSJ8807 12-0033 71 6834 WSJ8807 12-0023 72 6834 WSJ891017-0156 87 6834

WSJ890804-0138 92 6834 WSJ91 1212-0022 111 6834 WSJ87 1202-0 145 124 6834

The final ranking is obtained by merging the two

rankings by score. While some of the recall may be

sacrificed ('hot spot' retrieval has, understandably, lower

recall than full query retrieval, and this becomes the lower bound on recall for the combined ranking) the combined ranking precision has been consistently better than in either of the original rankings: an average improvement is 10-12% above the tf.idf run precision

(which is often stronger of the two).

CONCLUSIONS

We presented in some detail our natural language

information retrieval system consisting of an advanced

NLP module and a 'pure' statistical core engine. While

many problems remain to be resolved, including the question of adequacy of term-based representation of

document content, we attempted to demonstrate that the

architecture described here is nonetheless viable. In particular, we demonstrated that natural language processing can now be done on a fairly large scale and that its speed

and robustness can match those of traditional statistical programs such as key-word indexmg or statistical phrase

extraction. We suggest, with some caution imtil more

experiments are run, tiiat natural language processing can

be very effective in creating appropriate search queries out of user's initial specifications which can be fre- quentiy imprecise or vague. On the other hand, we must be aware of the limits of NLP technologies at our disposal. While part-ofspeech tagging, lexicon-based stemming, and parsing can

be done on large amounts of text (hundreds of millions of

words and more), other, more advanced processing

involving conceptual structuring, logical forms, etc., is

still beyond reach, computationally. It may be assumed

that these super-advanced techniques will prove even more effective, since they address the problem of

representation-level limits; however the experimental

evidence is sparse and necessarily limited to rather small

scale tests (e.g., Mauldin, 1991).

ACKNOWLEDGEMENTS

We would like to thank Drama Harman of NIST

for making her PRISE system available to us. We would

also like to thank Ralph Weischedel and Heidi Fox of

BBN for providing and assisting in the use of the part of

speech tagger. This paper is based upon work supported

by the Advanced Research Project Agency under Contract N(X)014-90-J-1851 from tiie Office of Naval

Research, under Contract N00600-88-D-3717 from PRC

135
Inc., and the National Science Foundation under Grant

IRI-93-02615. We also acknowledge support from the Canadian Institute for Robotics and Intelligent Systems

(IRIS).

REFERENCES

Br(^lio, John and W. Bruce Croft. 1993. "Query Processing for Retrieval from Large Text Bases."

Proceedings of ARPA HLT Workshop. March 21-24,

Plainsboro, NJ. Church, Kennetii Ward and Hanks, Patrick. 1990. "Word association norms, mutual information, and

lexicography." Computational Linguistics, 16(1),

MTT Press, pp. 22-29. Crouch, Carolyn J. 1988. "A cluster-based approach to thesaurus construction." Proceedings of ACM

SIGIR-88, pp. 309-320.

Grefenstette, Gregory. 1992. "Use of Syntactic Context To Produce Term Association Lists for Text

Retrieval." Proceedings of SIGIR-92, Copenhagen,

Denmark, pp. 89-97. Crrishman, Ralph, Lynette Hirschman, and Ngo T. Nhan.

1986. "Discovery procedures for sublanguage selec- tional patterns: initial experiments". Computational

Linguistics, 12(3), pp. 205-215.

(jrishman, Ralph and Tomek Strzalkowski. 1991. "Information Retrieval and Natural Language Processing." Position paper at the workshop on Future

Directions in Natural Language Processing in Infor- mation Retrieval, (Chicago.

Harman, Donna. 1988. "Towards interactive query expansion." Proceedings of ACM SIGIR-88, pp. 321-331.

Harman, Donna and Gerald Candela. 1989. "Retrieving

Records from a Gigabyte of text on a Minicomputer

Using Statistical Ranking." Journal of the American

Societyfor Information Science, 41(8), pp. 581-589.

Hindle, Donald. 1990. "Noun classification from

predicate-argument structures." Proc. 28 Meeting of

the ACL, Pittsburgh, PA, pp. 268-275.

Kwok, K.L., L. Papadopoulos and Katiiy Y.Y. Kwan.

1993. "Retrieval Experiments with a Large Collection using PIRCS." Proceedings of TREC-1 confer- ence. NIST special publication 500-207. pp. 153-172.

Lewis, David D. and W. Bruce Croft. 1990. "Term Clustering of Syntactic Phrases". Proceedings of ACM

SIGIR-90, pp. 385-405.

Mauldin, Michael. 1991. "Retrieval Performance in Ferret: A Conceptual Information Retrieval System."

Proceedings of ACM SIGIR-91, pp. 347-355.

Meteer, Marie, Richard Schwartz, and Ralph

Weischedel. 1991. "Stiidies in Part of Speech Labeling." Proceedings of the 4tii DARPA Speech and

Natural Language Workshop, Morgan-Kaiifinan, San

Mateo, CA.pp. 331-336.

Sager, Naomi. 1981. Natural Language Information

Processing. Addison-Wesley.

Sparck Jones, Karen. 1972. "Statistical mterpretation of

term specificity and its application in retrieval." Journal ofDocumentation, 28(1), pp. 11-20. Sparck Jones, K. and E. O. Barber. 1971. "What makes

automatic keyword classification effective?" Journal of the American Society for Information Science,

May-Jime, pp. 166-175. Sparck Jones, K. and J. I. Tait. 1984. "Automatic search term variant generation." Journal ofDocumentation,

40(1), pp. 50-66.

Strzalkowski, Tomek and Barbara Vauthey. 1991. "Fast Text Processing for Information Retrieval."

Proceedings of the 4th DARPA Speech and Natural

Language Workshop, Morgan-Kaufman, pp. 346-

351. Strzalkowski, Tomek and Barbara Vauthey. 1992. "Information Retrieval Using Robust Natural

Language Processmg." Proc. of the 30th A(X Meeting, Newark, DE, Jime-July. pp. 104-111.

Strzalkowski, Tomek. 1992. "TTP: A Fast and Robust

Parser for Natural Language." Proceedings of the 14th International Conference on Computational

Linguistics (COLING), Nantes, France, July 1992.

pp. 198-204.

Strzalkowski, Tomek. 1993. "Natural Language Processing in Large-Scale Text Retrieval Tasks." Proceediogs of the Fust Text REtrieval Conference (TREC1), NIST Special PubUcation 500-207, pp. 173-187.

Strzalkowski, Tomek. 1993. "Robust Text Processing in Automated Information Retrieval." Proc. of ACXsponsored workshop on Very Large Corpora. Ohio

State Univ. Columbus, June 22. Strzalkowski, Tomek, and Peter Scheyen. 1993. "An

Evaluation of TTP Parser: a preliminary report."

Proceedings of International Workshop on Parsing

Technologies (IWPT-93), Tilburg, Netherlands and

Diubuy, Belgium, August 10-13.

136
Design and Evaluation of the CLARIT-TREC-2 System

David A. Evans^ ^ and Robert G. Lefferts^ ^Laboratory for Computational Linguistics and ^claRIT Corporation

Carnegie Mellon University Suite 200A, 319 South Craig St. Pittsburgh, Pennsylvania 15213-3890 Pittsburgh, Pennsylvania 15213-3726

1 Introduction

The CLARTT team used the opportunity of the TREC2 evaluations to explore several facets of the CLARIT

system. In particular, given the performance of the

CLARIT system on TREC-1 tasks (Evans et al. 1993), we

focused our attention on evaluating

1. fully-automatic processing of topics and potentiallyrelevant documents and

2. topic/query augmentation using CLARIT thesaurusdiscovery techniques.

All of the results we report in this paper follow from

straightforward applications ofbase-level CLARIT processing, utilizing essentially the same CLARIT components that were employed in the CLARIT-TREC1 system. The general improvements we observe in

CLARIT-TREC-2 processing are attributable to modifications (especially simplifications) in processing steps

and in the settings of system variables. In the following sections, we describe the CLARITTREC-2 system, report our official processing results,

and offer a brief analysis of performance. In addition,

we report on several subsequent experiments we have

conducted on the TREC-2 collection that test the parameters of the CLARIT-TREC-2 system and identify sources of irrunediate improvements in processing.

2 CLARIT-TREC-2 System Description

and Processing Method

The CLARIT-TREC-2 system reflects a re-organization

of the tools and techniques employed in the CLARITTREC-1 system. One of our principal goals was to streamline CLARIT processing and to establish a baseline method that is amenable to parameterization and

analysis. As a consequence, the flow of data in the

CLARrr-TREC-2 system is simple, straightforward,

and efficient; furthermore, all CLARIT processing is fully automatic.

2.1 Changes from TREC-1

The essential differences between the CLARTT-TREC-l

and TREC-2 systems are in the preparation and evaluation of queries (TREC-2 "topics") and the automation of

steps designed to identify and process potentially rel- evant documents for use in query augmentation. The

following summaries highlight these points.

• One-Pass Querying. The CLARIT-TREC-1 sys- tem employed a two-step process to retrieve

documents—a first pass for partitioning ("evoking") and a second pass for final ranking ("discrimination"). This has been eliminated in the

CLARrr-TREC-2 system. Querying takes place

in one step over the entire collection using vectorspace-retrieval methods.

• Automatic Query Creation. The CLARTT-TREC1 system was categorized as a "manual" system,

though the required manual intervention was minimal. In particular, users were expected to assign an importance coefficient (with possible values "1",

"2", or "3") to the CLARIT-parsed terms in a topic

statement and possibly also to add terms to or

delete terms from the CLARIT-generated list. In

the CLARIT-TREC-2 system, the importance coefficient is assigned automatically by simple heuristics (described below). While users are still free to modify coefficients or terms, such intervention

is not required. All "CLARTA" results reported

in this paper reflect processing in which queries were fully automatically prepared by the CLARIT

system, without review or modification.

• Automatic Retrieval Refinement. When processing ad-hoc queries, the CLARTT-TREC-l sys- tem required that the user evaluate a few of the

top-ranked retrieved documents. User-nominated

documents were processed to identify terms for use in supplementing the source query. In the

CLARTT-TREC-2 system, user evaluations are not

required. Initial querying is accturate enough to support the automatic processing of the highestscoring retrieved docimtents without 'inspection'.

137
• Sub-Dociunent Processing. The CLARTT-TREC1 system treated all documents as whole texts; retrieval 'scores' were calculated over full documents. The CLARIT-TREC-2 system treats all documents as collections of one or more subdocuments, operationalized as variable-sized uruts of approximately paragraph length. Such units are used as the basis for all statistical calculations and

for measuring 'similarity' to a query. A full document is assigned the score {e.g., for ranking) of the

highest-scoring sub-document it contains.

2.2 Processing Method

Figure 1 offers a schematic overview of processing in

the CLARTT-TREC-Z system. All topics were parsed

for noun phrases. These, in turn, were either manually

("CLARTM") or automaticaUy ("CLARTA") assigned

weights (values "1", "2", or "3") for 'importance'. The

terms for each topic were automatically supplemented

with terms from a (pseudo-)thesaurus, automatically

extracted from available known-relevant documents

(in the case of routing topics) or from the top-ranked

sub-doamients returned in a first-pass querying of the

TREC-2 collection (in the case of ad-hoc topics). All in- stances of retrieval took place over the applicable full set of documents, which had undergone an initial round

of CLARTT processing (parsing).

The CLARrr-TREC-2 system incorporates a vector- space retrieval system that uses several CLARITspecific techniques to improve retrieval results. The

principal techniques involve the use of (1) natviral- language processing to identify and normalize indexing terms, (2) fully automatic query augmentation

based on CLARTT thesaurus discovery, and (3) simple text-analysis heuristics to approximate the effect of more sophisticated discourse analysis of texts. These

techniques are described in greater detail in the following sections.

2.2.1 Natural-Language Processing

CLARTT natural-language processing (NLP) encompasses an iiiflectional morphological analyzer for word

recognition and normalization and a deterministic rule- based parser for phrase identification. For TREC-2 processing, only simplex noun phrases (MPs) were used.

Simplex NPs are phrasal constitutents that include the

modifiers and head novm(s) of an NP but not the posthead prepositional phrases, relative clauses, or verb

constructions. The CLARTT parser can provide a more

complex linguistic analysis of texts, but such additional

detail was not used in TREC-2 experiments.

Figure 1: Overview of CLARIT-TREC-2 Processing

138
2.2.2 Query Augmentation

CLARTT thesaurus discovery is a process that can identify a set of core, representative terminology in a col- lection of documents. If the documents are relatively

homogeneous topically, the resulting 'first-order' the- saurus can be regarded as a broad 'signature' of the

topic of the collection. Typically, the procedure is most

reliable when the collection is large—2 megabytes or more of text. In the CLARIT-TREC-2 system, however, thesaurus discovery was used on the relatively

small collections of relevant documents for each topic.

For each routing topic, the document collection used to establish a first-order thesaurus consisted of the train- ing set of relevant documents for each topic; for each

ad-hoc topic, a set of the top-ranked sub-documents

from among the documents automatically retrieved via an initial pass of querying over the corpus. Terms in

the discovered thesaurus were used to supplement the

terms in the source query (topic). Thus, in the case of

ad-hoc topics, the procedure represents an approach to query augmentation based on fully automatic feedback.

2.2.3 Text Analysis Heuristics

Texts can have complex and interesting discourse structure, which often provides clues about the 'topic(s)' and

the 'important' information in a document. In general,

it is very difficult to exploit text-structure information

reliably in retrieval tasks over large collections of heterogeneous documents. Nevertheless, the CLARTTTREC-2 system applied two simple processing techniques to topics and corpus documents to attempt to capture ii\formation encoded in rhetorical structure.

First, all training and corpus documents were

divided into paragraph-sized units called "subdoaaments". This procedure is sensitive both to the

'normal' demarcation of paragraphs (successive blank

lines) and also to the total length of the text (measured in

numbers of sentences). After documents are partioned

into sub-dociunents, the sub-document is taken as the

basic imit for subsequent processing, viz., in collecting

statistics and in scoring retrieval. Second, terms extracted from a topic description were assigned importance coefficients based on their

locations in the topic text. Terms found in the first paragraph are given a weight of 3; terms in the second

paragraph, 2; and all other terms, 1. Of course, both techniques exploit possibly idiosyncratic characterisitics of the TREC-2 processing task. The use of scoring over sub-docvunents is clearly sensitive to the TREC definition of docvunent relevancy,

viz., that a docxunent is relevant regardless of length if

it contains a single relevant sentence. Furthermore, automatic term importance weighting is possible only be- cause of the formal discourse structure of TREC topics;

it would not neccessarily apply to other presentations

of topics or queries and certainly would not apply to free text in general.

We observe, however, that there is no one set of techniques that will perform optimally in every

information-retrieval (IR) situation. We emphasize, in- stead, that one important measure of a system's utility

and adaptability is its ability to take advantage of ex- ploitable features in a given IR task.

2.3 System Performance Notes

All CLARIT-TREC-2 processing took place on DEC

3000/400 (ALPHA/AXP) workstations running DEC

OSF/L One system had 128 megabytes of RAM, one

64 megabytes, and two 32 megabytes. Realized per- formance was considerably slower than would be ex- pected given the clockrate (133.33 MHz) of the DEC

3000/400's CPU. In fact, because of suboptimal compilers for the 64-bit architecture, performance was per- haps two times slower than the maximum possible.

One pass over the entire TREC-2 collection for 50 topics (processed simultaneously) required approximately

four hours, rvmning on the four machines in parallel.

The processing of a single topic is proportionally faster,

requiring approximately 20 minutes on a single machine.

3 Results

The CLARTT team processed all the topics in both the

"routing" and "ad-hoc" categories and worked with

the full set of data. Two sets of results were submitted for each category, corresponding to the "manual"

("CLARTM") and fully-"automatic" ("CLARTA") processing approaches taken with the topics.

3.1 General Summary of Official Results

Table 1 gives the official CLARIT-TREC-2 system rout- ing results as reported by NIST. A graph of the

precision-recall curves for the two sets of results is given in Figure 2. The total nvmiber of documents

retrieved under the routing task was 6,785 (CLARTM)

and 6,811 (CLARTA), representing, respectively, 64.69%

and 64.93% of the total known relevants (10,489).

139
Table 2 gives the official CLARIT-TREC-2 system

ad-hoc query results as reported by NIST. A graph

of the precision-recall curves for the two sets of re- sults is given in Figure 3. The total number of documents retrieved xmder the ad-hoc query task was 8,229

(CLARTM) and 8,109 (CLARTA), representing, respectively, 76.30% and 75.19% of the total known relevants

(10,785).

The graph in Figure 4 shows the average precision

score for each process at N docimients, for selected val- ues of N. It should be noted that the maximum possible

precision score at 500 and 1,000 documents is less than

100%. In particular, the average number of relevants

per routing topic is 209.78; this corresponds to a maximum precision of 41.96% at 500 documents and 20.98%

at 1,000 documents. The average number of relevants

per ad-hoc query topic is 215.70; this corresponds to a maximimi precision of 43.14% at 500 documents and

21.57% at 1,000 doctmients.

Tables 3 and 4 provide another view of total per- formance. The numbers in each cell give the number

of times the CLARrr-TREC-2 system produced results above, equal to, or below the median for all TRECparticipant systems. Numbers in brackets give the

instances of 'extreme' performance—^best and worst

among all systems. For the routing topics, for example,

CLARTT retrieval results at 1,000 documents were better than the median 36 times in both "manual" and

"automatic" modes; CLARTT scored the maximum 10

and 11 times, respectively. For the ad-hoc query topics,

CLARTT retrieval results at 1,000 documents were better than the median 44 times in "manual" mode and 42

times in "automatic" mode; CLARTT scored the maximimi 4 and 9 times, respectively.

3.2 CLARIT Automatic vs. Manual Modes of

Processing

In both tasks (routing and ad-hoc querying), CLARTTTREC-2 automatic processing results are virtually identical to manual results. This confirms our hypothesis

that the principal contribution to performance derives

from (1) the base-level CLARIT process (using linguistic phrases as information units) and (2) the effect of

query augmentation via thesaural terms. On this lat- ter point, we note that, on average, the final query

vector for a topic will contain many more terms that

derive from thesaurus extraction than terms that derive from the source topic. In general, then, when

reliable information is available (as in sample known

relevants or highly-likely relevants returned in a first- pass retrieval), the CLARIT process will succeed in

finding good supplemental terminology for a topic

and the overall effects of manual intervention will be

minimized.^

Figures 5 and 6 illustrate the relative absence of a

positive effect for manual intervention in the selection

and weighting of query terms. There are approximately

as many instances of decreased performance as there

are instances of increased performance. Most topics

show very little percentage difference in numbers of

docvmients returned;^ this is especially underscored in the results for routing topics at 1,000 documents.

4 Analysis

4.1 CLARIT Precision

As in TREC-1, CLARTT precision-recall curves demonstrate very high precision at low percentages of recall. The first few documents returned by the system are ex- tremely likely to be relevant for the given topic. This

fact of CLARIT processing was successfully exploited

in the TREC-2 processing method: query augmentation was possible because there was, in general, a good

concentration of topic-relevant information among the

sub-docvmients of the first-pass returned doamients.

As shown in Figure 4, precision remains quite stable

for all methods across the first 30 documents retrieved

and is relatively high across the full retrieved set of

1,000 documents.

5 Query Augmentation Experiments

A distinguishing feature of the CLARIT-TREC-2 sys- tem is the use of fully-automatic query augmentation.

As noted above, the selection of terms for query augmentation depends on (1) the selection of a source set of

known- or nominated-relevant documents and (2) the

application of the CLARIT thesaurus-discovery procedure. Since the size (and quality) of the source set of documents can vary and since CLARIT thesaurusdiscovery processing can be adjusted to nominate rel- atively greater or fewer numbers of terms, the 'queryaugmentation' facet of the CLARIT process is a natural source of potential variation in system performance.

^ Of course, there may be some forms of manual intervention—^not utilized in the CLARTT 'manual' process—that would have effects dramatically better than the CLARTT automahc process. We know of no such process that can be applied efficiently to arbitrary topics and

databases, however.

^Indeed, even in the absolute number of documents returned for each topic—not shown in the figures—there is very little difference.

140
Offlolal XFIEC-2 Roaults CI_AF=»l"r TFlEC-2 : Routing F»©rfornnanc©

O.O O.l 0.2 0.3 0.4 0.6 0.6 0.7 O.S O.O 1 .O

Automatic Flouting <3> 1 OOO Documonts Average => .3269

K/lanual Flouting <S> 1 OOO Oocuments Average = . 3302

Figure 2: P-R Curve for Routing Over 1000 Docs—CLARTM and CLARTA

CLARTM Routing Queries, 51-100 CLARTA Routing Queries, 51-100

Total number of documents Total number of documents

over all queries over all queries

Retrieved: 50000 Retrieved: 50000

Relevant- 10489 Relevant- 10489

Rel-ret- 6785 Rel-ret- 6811

Interpolated Recall-Precision Averages: Interpolated Recall-Precision Averages

at 0.00 0.7791 at 0.00 0.8101

at 0.10 0.5825 at 0.10 0.5781

at 0.20 0.4987 at 0.20 0.4941

at 0.30 0.4505 at 0.30 0.4368

at 0.40 0.3848 at 0.40 0.3851

at 0.50 0.3295 at 0.50 0.3358

at 0.60 0.2739 at 0.60 0.2749

at 0.70 0.2109 at 0.70 0.2074

at 0.80 0.1683 at 0.80 0.1561

at 0.90 0.0951 at 0.90 0.0933

at 1.00 0.0126 at 1.00 0.0141 Average Precision (non-interpolated) Average Precision (non-interpolated) over all rel docs 0.3302 over all rel docs 0.3269

Precision Precision At 5 docs 0.6120 At 5 docs 0.6200 At 10 docs 0.5940 At 10 docs 0.6060 At 15 docs 0.5800 At 15 docs 0.5893 At 20 docs 0.5700 At 20 docs 0.5730 At 30 docs 0.5527 At 30 docs 0.5460 At 100 docs 0.4396 At 100 docs 0.4346 At 200 docs 0.3436 At 200 docs 0.3416 At 500 docs 0.2176 At 500 docs 0.2184 At 1000 docs 0.1357 At 1000 docs 0.1362

R-Precision (precision after R retrieved) R-Precision (precision after R retrieved) Exact 0.3642 Exact 0.3646

Table 1: Offical Results, Routing Topics 51-100

141
Official TFIEC-2 Results CLARn rF=*EC-2 : Ad-Moo F=>orformarBO©

o.e

o.o 0.1 0.2 o.a 0.4 0.6 0.6 0.7 o.a o.s 1.0 Recall Automatic Ad-Moc at 1 OOO Documents Avaraga = .3S64 i Manual Ad-Hoc at 1 OOO Ooc-.umants CD Average = .3383 i

Figure 3: P-R Curve for Ad-Hoc Queries Over 1000 Docs—CLARTM ar^d CLARTA

CLARTM Ad-Hoc Queries, 101-150

Total number of documents

over all queries

Retrieved: 50000

Relevant: 10785

Rel-ret; 8229

Interpolated Recall-Precision Averages:

at 0.00 0.7455

at 0.10 0.5811

at 0.20 0.5240

at 0.30 0.4622

at 0.40 0.4135

at 0.50 0.3593

at 0.60 0.2983

at 0.70 0.2312

at 0.80 0.1516

at 0.90 0.0874

at 1.00 0.0062 Average Precision (non-interpolated) over aU rel docs: 0.3383

Precision: At 5 docs: 0.5840 At 10 docs: 0.5740 At 15 docs: 0.5640 At 20 docs: 0.5590 At 30 docs: 0.5433 At 100 docs: 0.4846 At 200 docs: 0.3975 At 500 docs: 0.2601 At 1000 docs: 0.1646 R-Predsion (precision after R retrieved) Exact 0.3741

CLARTA Ad-Hoc Queries, 101-150

Total number of documents

over all queries

Retrieved: 50000

Relevant: 10785

Rel-ret: 8109

Interpolated Recall-Precision Averages:

at 0.00 0.7587

at 0.10 0.5676

at 0.20 0.5038

at 0.30 0.4489

at 0.40 0.3938

at 0.50 0.3455

at 0.60 0.2806

at 0.70 0.2172

at 0.80 0.1463

at 0.90 0.0827

at 1.00 0.0070 Average Precision (non-interpolated) over all rel docs: • 0.3264

Precision: At 5 docs: 0.5800 At 10 docs: 0.5680 At 15 docs: 0.5547 At 20 docs: 0.5490 At 30 docs: 0.5253 At 100 docs: 0.4644 At 200 docs: 0.3874 At 500 docs: 0.2542 At 1000 docs: 0.1622

R-Precision (precision after R retrieved) Exact 0.3645

Table 2: Offical Results, Ad-Hoc Topics 101-150

142
Official TREC-2 Rsaulta Precision at N Docs

1

o.e —

o.e —

o.r —

Number of Documonts

Manual Routing Automatic Routing Manual Ad-Hoc CH Automatic Ad-Hoc

Figure 4: Comparative Precision at N Documents

>Median =Median <Median

CLARTM CLARTA CLARTM CLARTA CLARTM CLARTA

Precision 37 [1] 34 [0] 0 4 13 [0] 12 [0]

Rels in Top 100 32 [2] 29 [2] 6 8 12 [1] 13 [1]

Rels in Top 1000 36 [10] 36 [11] 5 6 9[0] 8[0]

Table 3: Summary of Results for Routing (51-100)

>Median =Median <Median

CLARTM CLARTA CLARTM CLARTA CLARTM CLARTA

Precision 38 [0] 34 [3] 4 7 8[0] 9[0]

Rels in Top 100 39 [1] 31 [0] 3 2 9[0] 17 [0]

Rels in Top 1000 44 [4] 42 [9] 2 2 4[0] 6[0]

Table 4: Summary of Results for Ad-Hoc Queries (101-150)

143

Using TREC-2 processing results as a baseline, we

have begun exploring several of the parameters of

query augmentation in the CLARIT-TREC-2 system.

The first set of experiments focuses on three parameters:

• The use of query augmentation compared to a procedure involving no query augmentation;

• The size of relevant document samples as source

collections for thesaurus extraction; and

• The 'threshold' chosen for the thesaurus discovery

process.

5.1 Query Augmentation vs. No Augmentation

In order to verify that CLARTT query augmentation

techniques do have a positive effect on retrieval results,

we have compared the official TREC-2 results against

experimental runs that do not take advantage of any

augmentation. For the routing queries, we re-ran the

routing task using only the raw queries as vectors (consisting of terms from the topic statement only). The

effects for manual and automatic modes are shown in

Figure 7. In the case of the ad-hoc queries, the vmaugmented results are simply the results of the initial round

of querjdng, before automatic feedback has taken place.

The effects for both modes are shown in Figure 8. Query augmentation using known-relevant documents, as in the routing experiment, has a dramatic ef- fect on system performance. The manual routing task

showed a 69% improvement with augmentation, while

automatic routing improved by 76%. Figure 9 shows

the effect of query augmentation for individual routing

topics, calculated as the difference in average precision

between augmented and unaugmented queries. Note

that very few queries do not improve with augmentation; most show great improvement.

Query augmentation is not as effective when used

for automatic feedback in processing ad-hoc queries,

but it still results in substantial improvements. For

manual ad-hoc queries, we found a 21% improvement

when using query augmentation; for automatic ad-hoc

queries, a 22% improvement. Obviously, the use of

known-relevant docioments in the case of routing topics has an impact on query augmentation, but it is not

the only important factor. The query-by-query effect of augmentation for ad-hoc queries is shown in Fig- ure 10. While the effect is not as great as with routing

topics, most queries show improvement with augmentation. By comparing the query-by-query results for automatic and manual modes, one can see that manual

intervention in query formulation improves the results of augmentation in specific cases, such as query 121,

but the positive effect is difficult to predict. Even without user review, we can have confidence in the ability

of query augmentation to improve query results, given

reasonably accurate initial query formulation.

We believe that the techniques we used in CLARITTREC-2 automatic feedback can be refined to give better documents as input to query augmentation. First,

due to engineering and time constraints in TREC-2

processing, we did not select the best sub-documents

from the entire corpus. Instead, we selected the best

sub-documents from a pool of the best relevant documents, which might not always correspond to the optimal set of sub-documents for query augmentation.

Second, the TREC-2 process used an absolute number of sub-documents—the top N—in query augmentation, regardless of the 'similarity' scores of those subdocuments to the source query. While it may not be possible to determine 'absolute' relevance on a query-byquery basis, minimum thresholds might be applied to exclude clearly irrelevant sub-documents, should such

be within the N otherwise to be used in augmentation.

5.2 Source Sample Size

In the case of the submitted results of CLARIT-TREC-2

processing, the thesaurus for each routing query was

extracted from the set of all its known-relevant train- ing documents. However, we can imagine that many

such documents contain some—perhaps a great deal

of—information that is not relevant to the topic at hand. (This is especially expected with long documents.) Therefore, we have experimented with alternative approaches to sampling text from known-relevant

documents. In particular, we have used a ranking

of sub-documents (paragraphs) to nominate candidate

'good' relevant texts and have used variable numbers of

sub-documents as source text in thesaurus extraction. In practice, to select source text for the thesaurusdiscovery process for a topic, we run the raw topic vector as a query over the collection of relevant documents, which are partitioned into sub-docim\ents, and

return only the top A'^ relevant sub-docviments. (In cases where the topic has fewer than A'^ sub-documents in the

collection of relevants, all sub-documents are selected.)

Our hypothesis is that thesauri extracted from relevant

sub-documents will contain greater numbers of 'true- positive' terms related to the topic. We have run the

experiment for N = 100, 200, 300, 500, and 1000 as the

cutoff for selected sub-documents. Figure 11 gives a sample of the results for routing topics, including the

best case of 500 sub-documents.

While the differences for different N are not great,

certain trends are evident. First, it is clear that using

only the more similar sub-doctunents from a collec- tion of relevants gives better results than using aU rele- vant full documents. (This represents an important im145
provement over the method employed in the CLARTTTREC-2 system.) Second, larger sample sizes seem to perform better than smaller ones. In the experiments

that we ran, this effect peaks at 500 sub-docimients, but

such results probably interact with the average number

of relevant sub-docttments available. As noted previously, we would like ideally to use a variable number

of sub-documents for each query, but such an approach

requires an accurate measure of 'absolute' relevance.

5.3 Thesaurus Size

CLARTT thesaurus discovery techniques allow for the

selection (sampling) of greater or fewer numbers of

terms from a collection. In practice, the size of the

sample is determined by a real-number threshold between 0.00 and 1.00. A larger threshold results in the

inclusion of more general terminology from the document collection; a smaller threshold results in selection

of terms that are more specific to the collection. (Intuitively, such variation correlates with the 'breadth' or 'narrowness' of the thesaurus.) The set of terms se- lected at a larger threshold will always properly include

all of the terms that would be selected from the same

collection at a smaller threshold. In CLARIT-TREC-2

processing, the threshold was set at 0.50. Note, however, that in the document-sample-size experiments re- ported above, we used a threshold of 0.75. Using sub-document samples of relevant documents with N = 300 (reflecting the 'second-best' perfor- mance obtained in the experiments on sample size), we

have explored the effects of using different thesaurus

extraction thresholds—at 0.50, 0.75, 0.85, and 0.95 levels. Results for routing topics are given in Figure 12. As with sample-size variation, differences in per- formance between increments are not dramatic, but

trends do appear. First, the 0.50 thesaurus is clearly

inferior and actually performs very much like the baseline CLARIT-TREC-2 system. Such a result indicates

that much of the variation observed between baseline CLARIT-TREC-2 processing and our current 'best' technique is due to changes in thesaurus thresholds,

rather than changes in the document sample size. Sec- ond, while all of the 0.75, 0.85, and 0.95 thesauri have

the similar precision at low recall levels, the 0.75 the- saurus performs slightly better in the average case. From this we might hypothesize that the 0.75 value is close to the optimal threshold for thesavirus discovery

in the context of query augmentation.

Figure 13 gives the results for the automatic routing

task for the optimal number of sub-documents and the- saurus threshold compared to TREC-2 reported results,

compared to the unaugmented baseline. Here we can

see that a simple refinement to parameter setting yields

a 5.5% overall improvement in average precision and

9.1%, 8.8%, and 7.6% improvement in precision at 10%,

20%, and 30% recall levels, respectively. In addition,

there is a 6.3% improvement in total relevant documents (7,241 vs. 6,811). Furthermore, our experiments

suggest mecharusms, such as m.easures of absolute rel- evance, that might result in further significant gains.

6 Conclusion

The CLARIT-TREC-2 system has successfully demonstated the ability to operate as a fully-automatic IR

system. Since the performance differences between

CLARIT manual and automatic processing modes are

negligible, one can use CLARTT in fully-automatic

mode and expect high precision and very good recall on retrieval tasks. The TREC-2 results also demonstrate the efficacy of

the CLARIT technique of automatic query augmentation. It is generally difficult for a user to predict whether

the addition of terms to a query will have a positive or negative effect on performance. CLARTT query augmentation, using CLARIT thesaurus-discovery techniques, however, shows positive effects. Because the

technique is fully automatic, it can be applied either at the time of query formulation (if exemplary relevant

texts are known) or at the time of 'first-pass' retrieval. In either case, final results will be improved.

In several experiments, we have already identified

two simple adjustments to CLARIT parameters that

will improve performance beyond the CLARIT-TREC2 system baseline. The system is not yet optimized; we

expect to make other straightforward improvements.

Many text processing fvmctions currently available

in the CLARIT system or near completion were not

used on TREC-2 documents. In future evaluations, we

plan to utilize some of the more sophisticated functionality in the system. For example, we have been developing grammars for recognizing complex tokens such

as proper names, dates, times, monetary values, etc., but did not use token recognition modules in CLARITTREC processing. We believe that such token recognition will improve the results for qiieries involving

specific persons or time intervals. Finally, we have

also been experimenting with generating sub-corpusderived equivalence classes for words and terms. We

expect to use equivalence classes selectively to supplement thesaurus terms in query augmentation.

In sum, we believe that CLARIT-TREC-2 process- ing results demonstrate the power of CLARIT tools to solve IR tasks. The CLARIT-TREC-2 system represents

only one of many possible configurations of CLARIT

modules. In subsequent work, we plan on exploring

other configurations.

146
Effect of Query /Augmentation : Flouting Quieries

Figure 8: Effect of Query Augmentation on Ad-Hoc Queries

147
^Effect of QtJ^ry AuQmentation : Automatic Routing | O.T —

O.O —

o.e —

0.-4 —

0.3 — ^ 0.2 —

O.I — °:

-0.2 —1— '—'•—' '

'—'—'———'—'—'—'—

'—'—'—'—'.—'.—!——:'.——:—

_

Querv Number

0.7 —

O.O —

1 Effect of Query Augmentation : IVIanual Routing |

O-S ——

•p 0.3 — ^ o.z —

0.-1 — o-

-o.-i

Query fsj umt>G>r

Figure 9: Query-by-Query Analysis of Effect of Query Augmeritation on Routing Queries

Effect of Query Augmentation : Automatic Ad-Hoc

i

a: O.OB vuliiiiiiiiiin

Quory Number

Effect of Query Augmentation : IV/lanual AcJ-Moc

uililllHI

Query fsJumber

Figure 10: Query-by-Query Analysis of Effect of Query Augmentation on Ad-Hoc Queries

148
Figure 11: Effect of Sample Size (Routing Topics)

Figure 12: Effect of Thesaurus Size (Routing Topics)

149
7 Acknowledgements

CLARTT team participation in the TREC activities was made possible, in part, by grants from ARPA/NIST

and CLARTT Corporation. We received valuable

assistance from CLARTT-Project-team members Greg

Grefenstette, Steve Handerson, and William R. Hersh

and from CLARIT Corporation staff Armar A. Archbold, Michael Mclnemy, and NataSa Milic.

8 References

[Evans et al. 1993] Evans, David A., Lefferts, Robert G.,

Grefenstette, Gregory, Handerson, Steven K., Hersh,

William R., and Archbold, Armar A., "CLABUT TREC

Design, Experiments, and Results". In Dorma Harman

(Editor), The First Text REtrieval Conference (TREC-1).

NIST Special Publication 500-207. Washington, DC:

U.S. Government Printing Office, 1993, 251-286; 494-

501.

150
Bayesian Inference with Node Aggregation for

Information Retrieval

Brendan Del Favero

Robert Fung

Institute for Decision Systems Research

350 Cambridge Avenue, Suite 380

Palo Alto, CA 94306

idsr@netcom.com

1 Introduction

Information retrieval can be viewed as an evidential reasoning problem. Given a representation of a document

(e.g., the presence or absence of selected words and

phrases), and a representation of an information need (e.g., topics of interest), the problem of information retrieval is to infer the degree to which the document matches the information need. Since probability theory is the classical choice for automating evidential reasoning, probabilistic

approaches to information retrieval are natural and have

had a long history, starting in the 1960's (Maron & Kuhns,

1960).

In this paper we describe research that adapts and applies

Bayesian networks, a new technology for probabilistic

representation and inference, to information retrieval. The

technology has substantial advantages over older technologies including an intuitive representation and a set of efficient inference algorithms. We discuss the Bayesian

network technology and probabilistic information retrieval

in Section 2 of this paper.

Our research is directed at developing a probabilistic

information retrieval architecture that:

• is oriented towards assisting users that have stable information needs in routing (i.e., sorting through)

large amounts of time-sensitive material,

• gives users an intuitive language with which to specify

their information needs,

• requires modest computational resources (i.e., memory

and CPU speed), and

• can integrate relevance feedback and training data with

users' judgements to incrementally improve retrieval performance.

Towards these goals, we have developed a system that allows a user to specify: multiple topics of interest (i.e., information needs), qualitative and quantitative

relationships between the topics, document features that

relate to the topics, and quantitative relationships between

these features and the topics. The system runs on a Macintosh n computer and can use training data to estimate

any of the quantitative values in the system. We discuss the

particular methods we developed and used in our system in Section 3.

We participated in the exploratory group (Category B) of

the 1993 Text Retrieval Conference (TREC-2), sponsored

by the National Institute of Standards and Technology

(NIST). As a participant in the exploratory group, we were

tasked with working with a subset of the TREC-2 training

and test data. Our training data consisted of Wall Street Journal (WSJ) articles and our test data consisted of San

Jose Mercury News (SJMM) articles. We chose a subset of

10 topics out of the 50 TREC-2 routing topics to best

illustrate the methods and concepts we developed. The

choice of the 10 topics was reported to the TREC

coordinators prior to our training runs and, of course, prior

to our receipt of the test data. We generated routing queries

for each of the 10 chosen topics, trained against the WSJ

training set to improve our queries, and tested these queries

against the SJMN articles in the test data set.

Our system was developed entirely within the duration of

the TREC-2 project (January 93 to June 93) including the document handling, feature extraction, inference, and

reporting capabilities. Our TREC-2 effort consisted of the two authors. We describe the experimental set-up in Section 4 and the result of our test run in Section 5. We are very encouraged by the test results and have many

ideas for future research, which we discuss in Section 6.

2 Background

In this section we describe the Bayesian network

technology and outline the previous efforts in probabilistic

information retrieval.

2.1 Bayesian Networks

While probability theory provides a suitable theoretical foundation for evidential reasoning, a technology based on

probability theory that is computationally tractable and that includes an effective methodology for acquiring the needed

probabilistic information has been lacking. Recent

developments in Bayesian networks have provided these

features. As the name suggests, the technology is based on

a network representation of probabilistic information

(Howard & Matheson, 1981; Pearl, 1988).

A Bayesian network represents beliefs and knowledge

about a particular class of situations. The use of Bayesian

151
networks is similar to expert system technologies. Given a Bayesian network (i.e., a knowledge base) for a class of

situations and evidence (i.e., facts) about a particular

situation of that class, conclusions can be drawn about that

situation. The technology has been used in a wide variety

of situations, including medical diagnosis, military situation assessment, and machine vision. A Bayesian network is a directed acyclic graph where each

node represents a random variable (i.e., a set of mutually

exclusive and collectively exhaustive propositions). Each

set of arcs into a node represents a probabilistic dependence

between the node and its predecessors (the nodes at the other end of arcs). The primary technical innovation of

Bayesian networks is the representation, through the network's structure, of conditional independence relations between the variables in a network. This innovation not only provides an intuitive representation to acquire

probabilistic information but also renders inference

tractable for large numbers of real-world situations.

Inferences can be drawn from a Bayesian network with a wide variety of algorithms. There are exact algorithms

(Lauritzen & Spiegelhalter, 1988; Shachter, 1986; Shachter,

D'Ambrosio, & Del Favero, 1990) as well as approximate

algorithms (Fung & Chang, 1989) Inference algorithms

compute a probability distribution of some combination of

variables in the network given all the evidence represented

in the network.

Since the introduction of the Bayesian network technology,

several efforts have been made to apply it to information

retrieval (Fung, Crawford, Appelbaum, & Tong, 1990;

Turtle & Croft, 1990). The results are promising.

However, several recent innovations have been made in the Bayesian network technology that have not yet been

applied to information retrieval. The innovations involve

the representation of conditional independence relations

that are finer than those currently represented at the network level. One of these innovations is Similarity

Networks (Heckerman, 1991) developed by David

Heckerman at Stanford University. Another innovation for representing the relationship between variables, the "co- occurrence diagram," was developed on this project.

2.3 Probabilistic Information Retrieval

Most probabilistic information retrieval techniques can be

illustrated graphically through the use of Bayesian

networks. In a simple formulation, there are n topics of

interest (tj, ... , tn) and m identifiable document features

(fl, ... , frn). The information retrieval problem is to compute the posterior probability p(tj Ifj, ... , fjn) for each

topic, given a quantification (i.e., the joint probability p(ti,

... , tn)) of the frequency that the topics appear in the corpus and a quantification (i.e., the conditional probability

p(f 1, ... , fml ti, ... , tn)) of the relationship of the "presence" of a topic in a document and the "presence" of

features.

Figure 2. 1 is a Bayesian network representing a retrieval model with one topic of interest and three features. The

node t represents the events "the document is relevant to topic t" and "the document is not relevant to topic t." The

nodes fj represent the events "the feature fj is present in the document" and "the feature fj is not present (is absent) in the document." The prior probabilities of the events t and

not-t are stored in the t node. The conditional probabilities

p( fi 1 1

) are stored in each of the feature nodes.

Figure 2.1: The two-level Bayesian network model of

information retrieval

Because of the lack of arcs between the feature nodes, this diagram embodies the assumption, used in many

probabilistic systems, that the features are conditionally

independent of each other given the topic.

Using the Bayesian network form of Bayes' rule called arc

reversal (Shachter, 1986), the posterior probability of the event t can be computed. The inversion formulas are straightforward and computationally feasible (they are described in the Appendix). Figure 2.2 shows the network

in Figure 2.1 with the arcs reversed. It represents a model

in which knowing whether one or more of the features are present provides information (i.e., the posterior distribution on node t) about whether the topic is relevant to the document.

Figure 2.2: The Information retrieval model after Bayesian inversion

To address multiple topics within this framework, a model

similar to the one represented by Figure 2.1 would be

generated for each topic, and inference would be carried out on each topic separately. However, these multiple models

152
fail to represent possible relationships between the topics.

As a consequence, the acquisition of consistent feature- topic probabilities and the interpretation and comparison of

multiple topic probabilities are problematic. For example,

it would be impossible using this framework to compute the probability that a document was relevant to two selected

topics or to compute the probability that a document was

relevant to at least one of two selected topics.

Bayesian networks can be used to explicitly represent the relationship between topics. For example, consider Figure

2.3 in which the two topics ti and t2 are related.

Figure 2.3: Information Retrieval Model with Two

Related Topics

The Bayesian inference problem with multiple topics

becomes more complicated than before. To compute the posterior probability of each topic, all the other topics must

be removed through marginalization as well as reversing

the topic-feature arcs as before. In addition, any joint

distribution between the topics can be computed. To

perform these computations, a general inference algorithm

is needed.

However, there is a way to simply a multiple topic network

to look like a single topic network (Figure 2.1). The topics can be combined into a compound topic (node s in Figure

2.4) (Chang & Fung, 1989) whose range is all possible

present-or-absent combinations of tj and t2. An advantage

of this representation is that the same simple computational

formulas can be used as before. A disadvantage of this representation is that the intermediate query, s, must contain (in the worst case) 2"

states, representing all possible present-or-absent

combinations of its n parent topics. However, this worst

case is rarely seen, because the relationships between topics

are typically sparse. Building this intermediate query is one

of the innovations of our research and is described in Section 3.2.

Figure 2.4: Multiple-topic query represented as a single

compound-topic query

3 Retrieval Architecture

This section describes the information retrieval architecture we have developed.

The inputs to the system are the descriptions of topics of

interest and a set of documents to test. The output of the system is list of documents ranked by their degree of

relevance to each topic. The system computes the degree

of relevance as the probability that a document is relevant

to a topic, for every document-topic pair. To help in this task, the system is given a training set of documents to which relevance judgements have been attached.

There are several components to our retrieval system. The

feature extraction component, described in Section 3.1, translates a document from its raw text form to a simpler

internal representation that the system can use. The query

construction component, described in Section 3.2, translates the description of a set of topics into an internal representation. The document scoring component,

described in Section 3.3, uses Bayesian inference to calculate a measure of a document's relevance to a topic,

given the internal representations of both document and

topic.

3.1 Feature Selection and Extraction

Any observable characteristic of the text of a document that may be clearly defined (e.g., as either present or absent)

may be regarded as a feature. Our system looks for two

types of features in the text of a document: single words

and pairs of adjacent single words. If a feature appears at least once in a document, it is counted as "present."

Otherwise, it is counted as "absent."

The internal representation of a document is therefore a

binary vector, each element indicating the presence or absence of the corresponding feature in the document.

The system removes many common suffixes fi-om a word

using Paice's stemming rules (Paice, 1977). This means,

for example, that if either of the words "walking" or

153
"walks" is present in a document, the system considers the root word "walk" to be present in the document.

The system must be given a list of features for which to look. This target list was constructed in three steps. First, a

list of candidate features was generated from the descriptions of the 50 TREC-2 routing topics. The text of

the descriptions was broken up into individual words, from

which the suffixes (if any) were removed. Duplicate words

and common words (stop words) were removed. A number

of two-word features (such as phrases and proper names) were identified by hand. This procedure created a list of

about 1400 candidate features.

Second, the system extracted the relative frequency

information for each of these features from the training

documents. Then, for each topic (the 10 plus an additional

topic representing "none of the above,") the system sorted

the candidate features in descending order according to the F4 formula of Robertson and Sparck Jones (Robertson &

Sparck Jones, 1976), which we used is a measure of the

ability of a feature to characterize a topic.

Third, the top 30 features for each topic (and the top 60

features for "none of the above") were combined into a

single list. After removing duplicates, this yielded the final

list of 229 features. We tried numerous feature selection strategies and settled on this one as the most satisfactory.

3.2 Query Representation and Construction

In preparation for the inference step, the 10 single-topic

queries are combined into one multiple-topic query. As

mentioned in Section 2.3, this aggregation can in the worst

case require 2 different states in the multiple-topic query.

In this section, we describe how to reduce the number of

states required by considering the relationships between the

topics.

Once the states of the query have been structured, we must

assign numerical values to the Bayesian model. We

describe how to generate estimates of the prior probabilities

of each of these states in Section 3.2.3. We describe in Section 3.2.4 how to define, for each feature and state, the conditional probability that the feature appears in a document given that the document is relevant to that state.

3.2.1 Pairwise Relationships Between Topics

There are six possible pairwise relationships between any

two topics t

J and t2'

1. ti and t2 are mutually exclusive: if there is no document relevant to both topics

2. tps a subset of t2 if all documents relevant to tj are

also relevant to t2

3. t2 is a subset of t j if all documents relevant to t2 are also relevant to tj

4. t^ and t2 are equivalent if tj and t2 are subsets of

each other (they satisfy Relations 2 and 3)

5. tj and t2 are dependent if knowing that a document

is relevant to tj gives you some information about

whether the document is relevant to t2

6. 1 1 and t2 are independent if knowing that a document is relevant to tj gives you no information on whether the document is relevant to t2

Each of the Relations 1-5 is a type of dependence between

topics. In a belief network, topics satisfying Relations 1-5 would be connected by an arc. To ensure that two topics

satisfy only one of the relations, Relation 5 (dependence) is defined as any type of dependence that is different from

those of Relations 1-4.

Relation 6, independence, is represented in a belief network

by the absence of an arc between the topics.

The distinction between dependence (Relation 5) and

independence (Relation 6) is useful in calculating the

probabilities of combinations of the topics, as described in Section 3.2.3.

Relations 1-4 can be identified by testing whether the defining condiuons are satisfied in the training set. If the

topics satisfy none of the first four relations, then a chi- square test can distinguish between Relations 5 and 6. If there are too few documents with relevance judgements for both topics to make reliable conclusions (our cutoff was 13 documents), the system makes an assessment, then prompts

the user to verify it manually.

To fully explore the pairwise relationships between topics, we selected ten of the fifty TREC-2 routing topics to use in our models and tests. The topics are listed in Table 3.1.

154
Topic Xonir Dpsrrintion

57 Financial Health of MCI

61 Israeli Role in the Iran-Contra Affair 74 Conflicting Policies of the US Government

85 Official Corruption in any government

88 Crude Oil Price Trends

89 Downstream mvestmg by OPEC members

90 Data on Proven Reserves of Oil and Gas

97 Fiber Optic Applications

98 Fiber Optic Equipment Manufacturers

99 Iran-Contra Affair

Table 3.1: List of the 10 topics we selected

The ten topics were selected to provide an interesting

mixture of relationships among the topics. Before looking

at the relevance information in the training set, we assessed

these relationships manually, as shown in Table 3.2. The

table is symmetrical about the main diagonal.

57 61 74 85 89 90 97 98 99

57

6174

85

88

89

90

97

98

99

e i i d d

e d s sd

i d e d i i i i sd

i s d e i i i i sd

i i e d d i

i i d e d i d d e d i i e d

d i i d e

sd sd sd i i e

d = dependent

s = subset

sd = subset or dependent

i = independent

e = equivalent

empty = mutually exclusive

Table 3.2: Pairwise relationships between 10 TREC topics, assessed manually before looking at the training data

Table 3.3 shows the topic relationships that were generated

automatically from the relevance judgements on the

training documents. Manual verification of the system's assessment was required in about half of the cases (marked

with an asterisk). The table is symmetrical about the main

diagonal.

57 61 74 85 88 89 90 97 98 99

57 e

* d d d*

61 * e d s * * * * d

74 d d e d

* d

85 s d e

* * d* d

88 * * e i i * * *

89 i e i * * onyu i i e 97 d

* * * * * e d* 98 d

* d* * d e

*

99 * d d d

* * * * * e

* = manual intervention required

Table 3.3: Pairwise relationships determined from the data

The system's assessments match the manual assessments

quite well. The system found even more mutually

exclusive pairs than we had intuitively thought. One

surprise is between topics 61 ("Israeli role in the Iran- Contra Affair") and topic 91 ("Iran Contra Affair"). One

might assume that 61 is a subset 99, but there are indeed

documents in the training set that are relevant to 61 but not

to 99. Thus, the relation between 61 and 99 is dependence

rather subset.

The information in Table 3.3 can be expressed as a directed graph, as shown in Figure 3.1. Each topic is a node. Two

topics are connected by an arc if there is at least one document containing them both. There is no arc between

mutually exclusive topics. The arcs marked "i" connect

independent topics, the unmarked arcs connect topics that are dependent, and the directed arc points to a subset from

its superset.

Figure 3.1 is not a belief network. It may be considered a "co-occurrence diagram," since topics that are relevant together (that co-occur) in the collection are connected.

88 89

90

Figure 3.1: Relationships diagram among 10 TREC topics

1^ 155
3.2.2 Generating states from the topics

The states of the multiple-topic query can be generated

automatically by enumerating all possible "relevant or not

relevant" combinations of topics, subject to two rules:

1. Two mutually exclusive topics cannot both be

relevant within the same state.

2. A topic that is a subset of another cannot be

relevant within a state unless its superset topic

is also relevant.

By construction, there is always one state, U', to which a document is relevant if it is relevant to none of the topics.

For the ten topics, these rules reduce the number of states drastically from the theoretical maximum of 1024 = 2^^

states to the actual number of 28 states. A state is identified by listing the topics to which a document is relevant (or not relevant) if it is relevant to that

state. For instance, documents relevant to the state (61 -74 85 -99) are relevant to topics 61 and 85 and are not

relevant to topics 74 and 99. The list of states appears in the first column of Table 3.4.

3.2.3 Generating State Priors

In theory, the prior probabilities of the states can be

calculated from their relative frequencies in the training set. However, since there are very few documents in the TREC2 training set that were evaluated for three or more topics, a

different way to estimate the priors was required.

One estimate is provided by factoring the states prior into products of the priors of smaller compound topics. This can be accomplished without manual intervention. For

instance, for the independent topics 88, 89, and 90, only

three numbers are needed (the prior probabilities of the three topics in the training set) to compute the probabilities

of all seven states containing the three topics. For the state (61 -74 85 -99), two numbers are needed: the prior

probability of the compound topic (-74 85 -99) and the probability that topic 61 is relevant given that topic 85 is relevant. The priors obtained by this method are shown in the second column of Table 3.4. They are expressed as inverse frequencies: the number given is the number of

weeks (on average) between articles relevant to a state, assuming 1000 documents per week.

state Average

Weeks between

Articles

,

Assessed

Automatically

Average

Weeks between

Articles

,

Assessed

Manually

-88 -89 90 < 1 3 -88 89 -90 < 1 3 -88 89 90 20 7

00

00

00

00

-89

-89

-90

90

< 1 20

2

4 88 89 -90 40 4 88 89 90 4000 9 57 97 98 10 8 57 97 -98 < 1 5 57 -97 98 < 1 6 57 -97 -98 < 1 3 -57 97 98 1 6 -57 97 -98 " ' " -57 -97 98 < 1 3 61 74 85 99 33 20 -61 74 85 99 14 3 61 74 85 -99 < 1 50 -61 74 85 -99 < 1 3 61 -74 85 99 < 1 20 -61 -74 85 99 < 1 5 61 -74 85 -99 5 100

-61 O J

Q Q 2 2 74 -85 99 < 1 3 74 -85 -99 < 1 2 -74 -85 99 < 1 3 57 74 < 1 20

85 98 1 50

U

Table 3.4: The states and their prior probabilities

expressed as frequencies (assuming 1000 articles per week) generated by two assessment methods

However, these priors proved unsatisfactory and required

manual override, for three reasons. First, the training set is a very biased sample of the WSJ. The training documents

were selected by the retrieval systems of TREC-1 to be

intentionally relevant to at least one of the topics. Thus, the

prior derived from the training set tends to be a gross overestimate of the true prior.

Second, the time period of the training set (1987 to 1992) is different from that of the test set (only 1991). The

frequency of the topics relative to each other changes over

time. For instance, there are many fewer articles on the Iran-Contra affair (relative to the other topics) in 1991 than

there were in 1987-1990. We adjusted the priors to match

the relative frequencies expected in the test set.

156
Third, the priors are widely divergent from the intuition of

any user who reads a newspaper. The numbers suggest, for

instance, that about 1 out of every 100 articles in the WSJ

(and, by analogy, in the SJMN) are relevant to topic 57,

"MCI." Any reader of the WSJ or the SJMN knows that

this estimate is much too high - the relative frequency is probably closer to 1 in 500 or 1 in 1000 than to 1 in 100.

Thus, the prior probabilities were assessed manually. It is assumed that the user has some knowledge of the test domain (in this case, articles in the SJMN) and can with some thought assess the relative frequencies of various

states as a part of specifying the query for the particular

routing request. For each state, we ask the user what is the average number of weeks between the publication of

articles relevant to that state. This number is presented in the third column of Table 3.4. It can be converted to a prior

probability by combininbg it with the assumption that there

are 1000 documets per week.

The prior probability of U , p( U ), is calculated as one minus the sum of the priors of all the other states, to ensure

that the probability of all states together is one.

3.2.4 Feature Conditional Probabilities

The inference algorithm requires, for each feature and for each state, the conditional probability of the feature given

the state. These probabilities cannot be obtained directly

from the relative frequencies obtained from the training set, because there are few documents that are relevant to more

than one topic at a time. We approximate these probabilities by using a structure called a noisy-or gate.

The noisy-or gate combines the effects of two or more

factors, each of which may contribute to the presence of a

feature. It is a model of disjunctive interaction, as described in (Pearl, 1988). It has been used in medical

decision research to calculate the probability of a particulai"

symptom being present, given diseases that cause the symptom (Heckerman, 1989).

In the context of information retrieval, a feature may be

present due to any of the topics that are relevant in the state. For each state-feature pair, we build a noisy-or model. The

contributing factors are the topics that are relevant within

the state. The effect is the feature's presence or absence in the document.

For example, consider a feature f and the state (57 -97 98). The feature may be present due to topics 57 or 98. It cannot be present due to topic 97 because that topic is not

relevant within the state. Let El be the event that the

feature is present due to topic 57, and let E2 be the event

that the feature is present due to topic 98. Table 3.5 lists all the possible cases of the two uncertain events. Figure 3.4 shows the belief network structure of the noisy-or model.

The node with the double wall is a deterministic logical or

gate.

Feature

Present due to 57

(El)

Feature

Present due to 98

(E2)

Feature

Present at all (El OR E2)

Yes Yes Yes

Yes No Yes

No Yes Yes

No No No

Table 3.5: Possible cases for a noisy-or node

Figure 3.4: Belief Network corresponding to a Noisy-Or

Gate Model

The only case in Table 3.5 in which the feature is absent is the fourth case. Thus, the conditional probability that the

feature is absent, given this state, is the probability of that fourth case. The probability that the feature is present is one minus the probability that the feature is absent.

3.3 Document Scoring

The Bayesian inversion described in Section 2.1 yields, for each document, the posterior probability that the document

is relevant to each state. We calculate the posterior

probability that the document is relevant to each topic by

summing the posterior probabilities of all of the states in which the topic appears.

For example, the posterior probability for topic 57 is the sum of the posterior probabilities of the five states in which

topic 57 is relevant (refer to Table 3.4). The states are (57

97 98), (57 97 -98), (57 -97 98), (57 -97 -98 -74) and

(57 74).

The final list of documents for each topic contains the top

1000 documents, ranked in descending order according to the posterior probability that they are relevant to that topic.

157
4 Experimental Environment

4.1 Hardware and Software Environment

The system was developed by one programmer over the period of six months. It was written in C using the Symantec Think C 5.0 compiler under Macintosh System

7.0.1. The experiments were run on two machines,

depending on availability: a Macintosh Ilfx and a Centris 650. Both machines had 8 Mb of memory and a 500 Mb

hard disk.

4.2 Summary of System Data Structures

Table 4. 1 is a list of the data structures used by the system.

Each is listed with the sections of this paper in which it is described and with an indicator of whether it was provided

by NIST (N), assessed manually by us (M), or generated

automatically by the system (A).

Data Structure. (Sections Source")

Descriptions of Routing Topics (3.1, N)

Training Documents (3.1, N)

Candidate Feature List (3.1, A/M)

Final Feature List (3.1, 4.3.2, A)

Relevance Judgements on the Training Documents

(3.2.1, N)

Topic Relationships (3.2.1, A/M)

State Prior Probabilities (3.2.2, 4.3.2, M)

Feature Conditional Probabilities (3.2.4, A)

Test Documents (3.3, N)

Document Posterior Probabilities (3.3, A)

Final list of retrieved documents (3.3, A)

Table 4. 1 : Data Structures used by the System

The candidate feature list could be defined entirely

automatically by sufficiently intelligent procedures such as phrase and proper name identification. The state prior

probabilities are the only data items that must be assessed manually.

The data files needed by the system, other than those provided by NIST, occupy about 100 Kb on disk.

4.3 Training Phase

4.3.1 Building tlie Data Structures We are a Category B participant in TREC-2. As such, we

used a subset (just the WSJ articles) of the full training

collection. Of these we used only those (roughly) 29,000

articles for which there is some relevance judgment

available. In addition, because we considered only ten of

the TREC-2 routing topics, we used only the 5941

documents that have a relevance judgment on at least one

of the ten topics.

The inputs to a training run are the candidate feature list, the list of topic relationships, the training documents, and

the training relevance judgements. The outputs of a

training run are the final list of features and the feature conditional probabilities.

The time to complete a training run is about 1.5 hours.

4.3.2 Defining the Query

The design of the TREC-2 experiment required that before we receive the test data, we submit the definitions of our system and of our particular query to NIST. The query

definition includes the final feature list, the list of topic

relationships, and the state prior probabilities. All of the other data is determined automatically, as shown in Table 4.1. We ran about two dozen sample experiments that applied

the system to the training documents to gauge its performance with different query definitions. In these tests, we varied only two of the data inputs, the state prior

probabilities and the final feature list. The list of topic

relationships remained constant throughout these tests.

It took much longer than expected to finish programming

the system. Thus, all of the experiments to define the query were completed in the two weeks immediately preceding

the final submission of our query definition to NIST.

4.4 Test Phase

As a Category B participant in TREC-2, we ran our routing

experiment on just the SJMN articles.

The inputs to the test runs were the final list of features, the

feature conditional probabilities, the list of topic

relationships, and the test documents. The output of the test run is the final list of retrieved documents.

A test run takes about 5.5 hours. This includes

decompressing the 150 MB of test data, one file at a time,

when it is needed.

5 Results

The TREC-2 designation for our system is "idsra2." Figure

5.1 shows the precision-recall curves for the ten topics we

considered, excluding topic 88, for which no TREC-2

participant found any relevant documents.

Table 5.1 shows several measures of performance for our

results, along with the best, median, and worst values for those measures among all TREC-2 participants. We again

exclude topic 88.

158
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 O.B 0.9 1.0 Recall Figure 5.1: Precision vs. Recall for system idsra2

Topic

Number

Relevant Retrieved at 100

idsra2 Best Median Worst

57 17 18 17 0 61 19 19 9 0 74 6 16 6 0 85 33 54 33 1

89 2 3 2 0

90 1 1 0 0

97 25 28 18 1 98 24 24 17 0

99 60 60 52 0

Table 5.1a: Relevant documents in the top 100 retrieved, for idsra2 and for all systems

Topic

Niimber

Relevant Retrieved at 1000

idsra2 Best Median Worst

57 18 19 18 0

61 24 25 24 0

74 11 31 11 1

85 88 115 88 2 89 2 4 2 0

90 1 3 0 0

97 27 32 27 1

98 26 29 26 0

99 70 70 66 0

Table 5.1b: Relevant documents in the top 1000 retrieved, for idsra2 and for all systems

Topic

Number

Average Precision

idsra2 Best Median Worst

57 0 387 0 460 0 374 0 . 000

61 0 464 0 464 0 083 0.000

74 u nU nU R0 nu m/I U / 4i U

n n p u u o 0.000

85 0 174 0 353 0 174 0.000

89 0 081 0 259 0 077 0 . 000

90 0 025 0 025 c 000 0 . 000

97 0 383 0 383 0 202 0 . 002

98 0 282 0 427 0 334 0 . 000

99 0 700 0 700 0 509 0 . 000

Table 5.1c: Average precision (as defined for TREC-2),

for idsra2 and for all systems

6 Conclusions and Future Directions

We believe that we have made significant progress to developing an information retrieval architecture that:

• is oriented towards assisting users with stable information needs in routing large amounts of timesensitive material

• gives users an intuitive language with which to specify

their information needs

• requires modest computational resources, and

• can integrate relevance feedback and training data with

users' judgements to incrementally improve retrieval performance.

We are encouraged by the test results. We have not had

very much time to analyze the results but we intend to try to understand why we did very well on some topics and not so

well on others. Very preliminary analysis suggests that the

features for the topics in which we did well (e.g., 61 and

99) were much more informative than the ones on which we did very poorly (e.g., 74).

We have many ideas for future research. These ideas fall into three basic categories: probabilistic representation, user interface, and inference methods.

The most important improvements we would like to make

are in the category of probabilistic representation of the topic and the document. One research goal is to develop a way to intuitively represent relationships between features. Also, we would like to explore more sophisticated feature extractors that recognize phrases, synonyms, and features derived from natural language processing. We believe that achievement of these goals could lead to significant

improvements in performance.

159
In conjunction with these representational improvements, we would like to design a complete user interface that would allow users to make the needed probabilistic

judgements easily and intuitively. We would also like to develop an explanation facility that could describe why one document was preferred over another.

There are new exact and inexact algorithms that could

handle these representational modifications. We would like

to experiment with these algorithms to see if they are

suitable. We would also like to implement a relevance

feedback mechanism based on the Bayesian concept of

equivalent sample size.

Finally, while the information retrieval problem has been

viewed primarily (and rightly so) as an evidential reasoning

problem, we take the position that a decision-theoretic perspective is more accurate since information retrieval is a decision process. We believe that this perspective can

provide additional insight and eventually improve upon the

probabilistic approaches that have been developed.

Acknowledgment

We would like the thank Dr. Richard Tong for his assistance and guidance.

Appendix Bayesian Inference

We calculate the posterior probability that a document is relevant to a state using Bayes' rule and an assumption of

conditional independence between the features.

The Bayesian inversion formula is this:

p( s I document ) = p( s I F ) = ^

f

^ ^ ^

, P( F )

where s is the state, and F is a binary feature vector with

Fj = 1 if feature i is present ( the event fj"^ )

Fj = 0 if feature i is absent ( the event fj ).

The set of all features that are present is denoted F"*". All other features are absent and are in the set denoted F".

The first term in the numerator is expanded under the assumption that the features are conditionally independent

given the state.

p( F I s ) = f7 P( ' s ) IT P(

^"

' s )

i in F^ i in F'

The second term in the numerator is the state prior, p( s ), which can be estimated as described in Section 3.2.

The denominator is a normalization constant obtained by

summing over the values for the numerator for all the

states.

P(F)= I p(Fls)p(s)

all s

p( F ) is the probability that one would observe the

particular set of features F.

References

Chang, K. C, & Fung, R. M. (1989). Node Aggregation for

Distributed Inference in Bayesian Networks. In IJCAI 89.

Detroit: Morgan Kaufmann.

Fung, R. M., & Chang, K. C. (1989). Weighing and

Integrating Evidence for Stochastic Simulation in Bayesian

Networks. In R. D. S. M. Henrion L.N. Kanal and J.F. Lemmer (Eds.), Uncertainty and Artificial Intelligence 5

Elsevier Science Publishers B.V. (North-Holland).

Fung, R. M., Crawford, S. L., Appelbaum, L., & Tong, R. M. (1990). A Probabilistic Concept-based Architecture for Information Retrieval. In Proceedings of the ACM

International Conference on Information Retrieval

Brussels, Belgium.

Heckerman, D. E. (1989). A tractable algorithm for diagnosing multiple diseases. In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, (pp.

162-173). Detroit, MI.

Heckerman, D. E. (1991). Probabilistic Similarity

Networks. The MIT Press.

Howard, R. A., & Matheson, J. E. (1981). Influence

diagrams. In R. A. Howard &. J. E. Matheson (Eds.),

Readings on the Principles and Applications ofDecision

Analysis (pp. 721-762). Menlo Park, Ca.: Strategic

Decisions Group.

Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local

computations with probabilities on graphical structures and

their application to expert systems. JRSS, 50, 157-224.

Maron, M. E., & Kuhns, J. L. (1960). On relevance,

probabilistic indexing and information retrieval. Journal of

the ACM, 7, 216-244.

Paice, C. D. (1977). Information Retrieval and the Computer. London.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent

Systems. San Mateo, Ca.: Morgan Kaufman.

160
Robertson, S. E., & Sparck Jones, K. (1976). Relevance

Weighting of Search Terms. Journal of the American

Society for Information Science(May-June), 129-146.

Shachter, R. D. (1986). Evaluating influence diagrams.

InOperations Research (pp. 871-882).

Shachter, R. D., D'Ambrosio, B., & Del Favero, B. (1990).

Symbolic Probabilistic Inference in Belief Networks. In AAAI-90, (pp. 126-131). Boston: Morgan Kaufmann.

Turtle, H., & Croft, W. B. (1990). Inference networks for document retrieval. In J. L. Vidick (Ed.), ACM SIG IR,

(pp. 1-24). Brussels, Belgium.

Effective and Efficient Retrieval from

Large and Dynamic Document Collections

Daniel Knaus, Peter Schauble

(knaus|schauble)@inf.ethz.ch

Swiss Federal Institute of Technology (ETH)

CH-8092 Ziirich, Switzerland

Abstract

A new retrieval method together with a new access structure is presented that is aimed at a high update

efficiency, a high retrieval efficiency and a high retrieval

effectiveness. The access structure consists of signatures

and non-inverted descriptions. This access structure can be updated efficiently because the description of a single

document is stored in a compact form. The signatures

are used to compute approximate retrieval status values

first, and the non-inverted descriptions are then used to determine the final list of documents ranked by the ex- act retrieval status values. Our basic approach based on the standard tf * idf weighting scheme has been improved in in both retrieval effectiveness and retrieval ef- ficiency. On an average, the time for retrieving the top

ranked document is clearly below two seconds while the

document collection can be updated in 10 msec, (inserting, deleting, or modifying a document description).

1 Introduction

Information retrieval systems are more and more used

to retrieve information from large and dynamic data

collections, e.g. in office automation or in newscasting companies. Since in such environments, the data

collections may have to be updated many times every

second, the update efficiency is an important evaluation

criterion that should be taken into account in addition

to the traditional evaluation criteria (retrieval effective- ness, retrieval efficiency, etc.) [10, pp. 161], [12, pp. 144].

Before introducing our approach, we show that the

update efficiency is conflicting with the retrieval effec- tiveness and with the retrieval efficiency. In the case of

static data collections, the retrieval effectiveness crite- rion is usually met by means of weighted retrieval in- cluding relevance feedback [4], [9] whereas the retrieval

efficiency criterion is met by precomputing the document descriptions and by organizing these descriptions

as an inverted file [5]. In the case of dynamic data collec- tions, however, a transaction updating the inverted file

may block other retrieval and update transactions for an

unacceptable long time because adding the description

of a new document to an inverted file is time consuming,

particularly if the document is long. A possible remedy

is to use signatures instead of an inverted file [2]. Sig- natures can be updated efficiently; however, they do

not allow document feature weighting and hence, the

retrieval effectiveness of the signature based method is inferior to a fully weighted retrieval method [8]. Thus,

it is difficult to achieve simultaneously high retrieval

effectiveness, high retrieval efficiency, and high update

efficiency. In our approach, we focus on the retrieval from large

and dynamic data collections where we face the problem of achieving simultaneously high retrieval effective- ness, high retrieval efficiency, and high update efficiency.

Addressing this problem is justified by the need for appropriate retrieval capabilities in dynamic environments

such as in office automation or in newscasting companies. Within the SPIDER project [ll] we have devel- oped a retrieval method and an access structure which

supports fully weighted retrieval and which achieves

short response times even when the collection is updated

several times every second. In Section 2, we describe our

basic approach, in Section 3, we focus on refinements of

this basic approach, in Section 4, we present the results,

and in Section 5, we draw some conclusions.

2 SPIDER'S Query Evaluation

Algorithm

In this section, we present a retrieval method to- gether with a new access structure facilitating both fast weighted retrieval and efficient updates of the access

structure. A preliminary version was presented in [11]. Our access structure consists of signatures and noninverted descriptions of the documents. The signatures

are used to compute approximate retrieval status values

RSV°{q,dj) first and the non-inverted document descriptions are then used to determine the exact retrieval status values RSV[q,dj). The trick is that the approx163
imate retrieval status values provide fairly tight upper

bounds for the exact retrieval status values. We will see how we can take advantage of these upper bounds such

that only a few exact retrieval status values have to be

computed.

The retrieval method determining the approximate

retrieval status values and the retrieval method deter- mining the exact retrieval status values are given by the

functions RSV'^ and RSV respectively. Let * ~ {(pO,...,(Pm-l}

be the indexing vocabulary (e.g. a set of terms) and let D := {do,...,dn-i}

be the current document collection. We assume that

the signatures a{dj ) consist of w bits where the bit at position p has the value <T{dj)[p]. = {a{dj)[0],...,<r(d^)[w-l])

Every indexing feature (pi is assigned a bit position p =

h{(pi) by means of a hash function h((p).

/i:*->{0,...,u;-l}

The function h specifies a signature cr(dj ) for every document dj by setting the bit at position p iff dj contains a feature <pi which is hashed to this position.

^y^-jJiyi ^0 otherwise

We now define the approximate retrieval status value RSV'^{q,dj) and the exact retrieval status value

RSV{q,dj) as follows.

RSV^{q,dj):=-^ * ^1 < * (1)

RSV{q,dj):= -ir- * ^ flij * 6i (2)

where a^j denote the approximate weights of document

features, Oij denote the exact document weights, and bi denote the query weights. The size | dj | of the description vector dj is defined as usual (see Table 1). Our basic approach consists of a tf * idf weighting

scheme, i.e. the query features are "ntn" weighted

whereas the document features are "ntc" weighted (see

Table 1). The query weights 6j depend on the feature frequencies ff{ipi , q) and on the normalized inverse document

frequencies nidf{(pi). The exact document weights Cij

depend on the feature frequencies ff{ipi,dj) and on the

nidf{(pi). In addition, they (oij) are cosine normalized,

which is expressed by the division by \dj \ in (1) and

Figure 1: Computation of the exact retrieval status val- ues in decreasing order of the approximate retrieval sta^ tus values.

(2). The md/((p,-)'s are determined by the document

frequencies df{(pi). The normalizations of the feature

frequency component and the normalizations of the document frequency component do not affect the retrieval status values because they cancel out when using the

cosine measure. Because of these normalizations, the approximate

document weights a°y are always upper bounds of the exact document weights Cjj , but they do not depend on

the feature frequencies ff{(Pi,dj).

0 < aij < Oij

0<bi

It is easy to show that RSV{q,dj) = 0 if £r(g) A

(T{dj) — 0. Furthermore, from the fact that <pi G dj

implies cr{dj)[h{(pi)] = 1 and from Oij < a^j follows

RSV{q,dj) < RSV°{q,dj) (3)

In what follows, we describe the evaluation algorithm

by means of an example. Let q be the user's query.

In a first step, bitwise AND-operations are performed

with the query signature a{q) and the signature cr{dj)

of every document dj G D. If <7{q) A o-{dj) is non-zero

then the approximate retrieval status value RSV°{q, dj)

is computed. The first step is finished by ordering the

documents in decreasing order of the approximate re- trieval status values. Remember that a{q) A <T{dj) = 0

implies that RSV{q,dj) = 0 and hence, dj can be ignored in the sequel. In the second step, the exact retrieval status values

are computed in the order that was previously deter- mined by means of the approximate retrieval status values. As shown in the following example, only as many

164
exact retrieval status values are computed as necessary. Assume that d-r is the first (i.e. dj has the highest

RSV^), diz is the second, and ds is the third document as shown in Figure 1. In this case, RSV{q,dT)

is computed first and next, RSV{q,dxz) is computed.

According to Figure 1, J?5V'(g, dia) is the highest ex- act retrieval status value of those two exact retrieval status values that are known at this moment. Fur- thermore, all unknown exact retrieval status values are

less than or equal to RSV^{<i,di) because of (3). Since

RSV{q,d\3) > RSV°{q,ds), we can conclude that dis

has the highest exact retrieval status value of all docu- ments. In this example, only two exact retrieval status values had to be computed to determine the top ranked

document. In practice, more exact retrieval status val- ues have to be computed.

The evaluation algorithm is based on an access struc- ture that specifies the following functions.

er : dj cr(dj) (4)

TT : K-» {{(Pi, ff{ipi,dj)) : (pi e dj} (5)

nidf : <pi h-> nidf{<pi) (6)

size : rfj (-»

I dj \ (7) The functions a and tt determine the signatures and the

non-inverted document descriptions respectively. The

functions nidf and size provide scaling and normalization factors. The function values (^{dj) and ir{dj) are determined completely by the document dj itself. The

function values nidf{{pi) and size{dj), however, depend

on the entire document collection. Fortunately, the vari- ation of nidf(<pi) is small if the data collection is suf- ficiently large. Thus, nidf[<pi) has to be recomputed

only if the domain of the data collection is shifting and

size{dj) has to be recomputed if either nidf was recalcu- lated or dj was modified. In the next section, we will see how this basic query evaluation scheme can be refined.

3 Refinements

The basic evaluation algorithm described in Section 2 achieves an excellent update efficiency because the description of a single document is stored in a compact

form which can be updated efficiently. However, the

retrieval effectiveness as well as the retrieval efficiency

need further refinements in the case of the TREC experiment where extremely large queries are matched against a large document collection containing some large documents.

To improve the retrieval effectiveness, we adapted the

basic evaluation algorithm to the best global weighting

scheme achieved by the SMART system at TREC-1 [1]. The query features are "Itn" weighted, i.e. the query

weights depend on the logarithm of the feature frequencies and on the inverse document frequencies, and they

are not cosine normalized. The "Inc" weighted document features depend on the logarithm of the feature

frequencies. They are cosine normalized, but they do

not have a document frequency component. The detailed definitions of the feature weights are given in Tar

ble 1. In order to achieve a good retrieval efficiency for the

TREC collection we have reduced the indexing vocabulary in such a way that on one hand the retrieval

efficiency is improved and on the other hand, the re- trieval effectiveness is not deteriorated very much. Our

indexing vocabulary was not only reduced by applying a stop word list and a word reduction algorithm,

but also by eliminating further indexing terms. From

another project [3] and from the experiences of the

SMART system at TREC-1 [1] we knew that the re- moval of the indexing features having a very high document frequency does not deteriorate the retrieval ef- fectiveness very much. The reasons why this restriction has a small effect on the retrieval effectiveness is the

following. The elimination of the high document fre- quency features changes the retrieval status values very

little because these features have a small inverse document frequency and hence, they contribute little to the

retrieval status values. Such an elimination of the high

document frequency features is similar to the application of a collection dependent stop word list in addition

to a general stop word list. Thus, this restriction has

only a small effect on the retrieval status values. In what follows, we focus on the retrieval efficiency

and why it is improved by this restriction. With a re- duced indexing vocabulary,

1. fewer documents have a positive approximate re- trieval status value,

2. fewer features (pi contribute an error (a^j — aij) * hi to the approximate retrieval status values, and

3. fewer false matches (caused by collisions) occur when computing the approximate retrieval status

values.

Both scanning the signatures and sorting the approximate retrieval status values become faster when fewer

approximate retrieval status values are positive. Fur- thermore, the approximate retrieval status values be- come tighter upper bounds when fewer false matches

occur and when the sum of the errors (o^^ — Oij) * hi

is reduced. Having tighter upper bounds means that

fewer exact retrieval status values have to be computed

until the top ranked document is determined. The re- trieval effectiveness and the retrieval efficiency achieved

by means of the reduced indexing vocabulary are pre- sented in the next section.

165
4 Experiments

In this section, we present the evaluation of the method

described above and we compare it to methods to other

weighting schemes. We focus on the efficiency of modifying documents and on the correlation between the re- trieval efficiency and the retrieval effectiveness. We will also see what is the influence of the vocabulary restric- tion on the retrieval effectiveness and on the retrieval

efficiency. For the final evaluations we concentrated on

the adhoc queries.

Before discussing the results, we define what we mean

by a partition, a run and an experiment. The document

collection has been split up into several partitions, each

consisting of at most 100,000 documents. Thus, the

large collections DOEl (Department of Energy, Disk 1) and ZIFF3 (Ziff-Davids Publishing, Disk 3) were divided

into three and two partitions respectively. A run consists of the evaluation of 50 queries (either the set of

routing queries or the set of ad hoc queries) against all documents of one partition. For each query, the 1000

top ranked documents have been retrieved. An exper- iment consists of several runs and the merging of the

lists of ranked documents for each query. For TREC-2,

the two sets of experiments "Topics 51-100 versus Disk

3" and "Topics 101-150 versus Disks 1 and 2" have been

evaluated.

All efficiency evaluations are based on CPU time

rather than on real time in order to eliminate side effects from other jobs running on the same machine. In these

experiments, we used a SUN SPARCserver MP690 with

128 MBytes RAM.

We derived the document descriptions directly from

the CD's. The indexing process included the elimination

of stop words (van Rijsbergen's stop list [12, pp. 18]) and

Porter's word reduction algorithm [6]. The normalized

inverse document frequencies have been derived from

the documents of disks 1 and 2 only. Uncompressing

and indexing a single document needs around 100 msec

on an average depending on the length of the document.

The computation of the inverse document frequencies

from the descriptions took about 1.5 hours of CPU time.

The average time for inserting a document description

into the access structure is on a scale of 10 msec - again

depending on the number of features ipi per document.

Inserting a document description into an inverted file would need more time because the postings had to be

inserted into the different lists associated to each fea- ture. The restriction of the vocabulary was accomplished

by omitting features occurring in more than 15% of all documents (from the disks 1 and 2), i.e. in more than

111'337 documents. We have chosen 15% of the collec- tion although also a stronger limit of 10% should not

affect the retrieval effectiveness [l]. In our experiments we compare the 15% limit ("dfl5") to a non restricted

vocabulary ("all").

We now have three parameters which can be combined to specify eight different retrieval methods. Each

method can be identified by a string built from the labels

for the document feature weighting, the query feature

weighting and the vocabulary restriction:

{doc.feat_weight) . {query,feat.weighi) . {vocab)

In what follows, we present the results of the following

nine methods:

MO ntc.ntn.all

Ml Inc.ltn.all M2 lnc.ltn.dfl5 M3 Inc.ntn.all M4 lnc.ntn.dfl5

M5 Itc.ltn.all M6 ltc.ltn.dfl5 M7 Itc.ntn.all M8 ltc.ntn.dfl5

First, we compare the retrieval effectiveness of our method (Ml) described in Section 2 to the standard

tf * idf method (MO) by means of the precision-recall

graph in Figure 2. As expected, the method Ml is more

effective than MO and achieves a retrieval effectiveness among the best methods presented at TREC-2. In order

to find out the reason for this difference in the retrieval effectiveness we must have a closer look at the infiu- ences of each parameter (document and query feature

weighting).

In Figure 3, the 11-pts average precisions of each

method (MO to M8) are plotted on the left axis, and

they are connected to the median response times (for

the top ranked document) plotted on the right axis. The

most obvious conclusion from this graph is the following: the higher the precision, the slower the response, and vice versa. The method MO performs clearly worse than the methods Ml to M8 in respect to both retrieval effectiveness and retrieval efficiency.

We concentrate on the response times of the top

ranked document because the response times of all fur- ther ranked documents are of secondary interest, since a user is supposed to read the top ranked document before looking at the other documents and the retrieval system can retrieve further documents while the user is reading the top ranked document.

We can also see from Figure 3, what are the influ- ences of the different parameters on the average precision. Regarding the weighting of the document features,

the "Inc" weighting achieves a 4-10% higher precision

than the "Itc" weighting. The "ntc" looses 5% of precision compared to "Itc" . In the case of query feature

weighting, again the logarithmic "Itn" weighting is more

166
Precision

1 —] 1

Ml Inc.ltn.all MO ntc.ntn.all

Figure 2; Precision recall graphs of the most effective method (Ml) and of the least effective method (MO).

effective than the "ntn" weighting (10-15%). Restricting the vocabulary results in a 2-7% lower precision. We

can summarize the experiences as follows:

• The md/(y>i)'s in the document feature weights have

a bad influence on the retrieval effectiveness. It could

be that for long documents the estimation of the

lengths \dj | is inappropriate when the nidf^fiYs are taken into account.

• Logarithmic feature weighting is more appropriate

for the long TREC documents and queries than lin- ear feature weighting. Logarithmic feature weighting

avoids an overweighting of features occurring very fre- quently within a document.

• Restricting the indexing vocabulary by ommitting fea- tures with a high document frequency df{(pi) has a

noticeable influence on the average precision.

In what follows, we discuss the influences of the differ- ent parameters on the response time (as shown in Fig- ure 3). Restricting the vocabulary accelerates the query

evaluation (by 9-14%) for the reasons described in Section 2. The "ntn" weighting of the query features is also 9-14% faster than the "Itn" weighting. For document feature weighting, the "Inc" weighting is 5-10%

slower than the "Itc" weighting. The "ntc" weighting of

is even slower. These results can be explained in terms

of the approximation error.

• It is obvious that for the "Itc" weighting the approximation error (o°j- —aij)*bi is smaller than for the "Inc"

average

precision

median response time for 1 . rank

Ml Inc.ltn.all

M2 lnc.ltn.dfl50.30

M5 Itc.ltn.all M3 Inc. ntn. all M6 ltc.ltn.dfl5

0.27

M4 lnc.ntn.dfl5

M7 Itc.ntn.all M8 ltc.ntn.dfl5 MO ntc.ntn.all

Figure 3: Average precisions and response times of the

first ranked document.

167
Partition Partition

API

AP2

DOEl.l

DOE1.2

D0E1.3

FRl

FR2

WSJl

WSJ2

ZIFFl

ZIFF2

1—CD—

I

• hUH •

• HHH •• h-cn—I •

rtH •••

KEI] 1 • \-CD 1 -

I—en—I'

HID—I

HU—

K

1 3 4 5

1

\—I—I—r—^

6 7 8 9 10 sec.

API

AP2

DOEl.l

DOE1.2

D0E1.3

FRl

FR2

WSJl

WSJ2

ZIFFl

ZIFF2

1.1

H4.2

H4.3

H4.4

3.4

H5.9

18.6 8.0

Hll.9

Hll.O

11.9

-1 ^

10 *10^

postings

Figure 4: Response times of the first ranked document

per run (adhoc queries versus the listed partitions) for the fastest method (M8 nltc.ntc.dfl5).

weighting because of the scaling with the nidf{<pi).

On the other hand, the approximation error for the

"ntc" weighting is very large compared to both the

"Itc" and the "Inc" weighting because of the normalized linear weighting instead of the normalized logarithmic weighting.

The box plots [7, pp. 336] presented in Figure 4 show

the distribution of the response times required to deter- mine the top ranked document. A box plot visualizes

the median (the line within the box), half of all samples

(the box), and outliers (the dots) of a sample collection. In our case we have 50 samples: the response times of

the 50 adhoc queries run against a partition. For most

queries the top ranked document was retrieved in less than two seconds. The few outliers were all produced by

the same queries (#103, #136, #138, #144). In general, the response times become shorter if a partition

contains less documents and if the document descriptions consist of less postings on an average (see Fig- ure 5).

5 Conclusions

In our approach we stressed the update efficiency. We

have shown that the retrieval effectiveness does not have

to be sacrificed to achieve a high update efficiency when

coping with highly dynamic document collections. Our

approach could probably be further improved by findFigure 5: Number of postings per partition.

ing a weighting scheme that, on the one hand, achieves a

very good retrieval effectiveness and that, on the other

hand, can be approximated by frequency independent

weights with only little variation from the exact weights.

The retrieval efficiency could be improved by better

partitioning the document collections according to the

lengths of the documents. Our approach seems to be

very amenable to parallel processing. We may think of

several configurations (partitioning the query, partitioning the document collection, etc.). At this moment, it is not clear which configuration is appropriate for which

requirements. Furthermore, we do not know yet how

dynamic the document collection must be such that our

access structure outperforms inverted files.

References

[1] C. Buckley, G. Salton, and J. Allan. Automatic Retrieval With Locality Information Using SMART.

In TREC-1 Proceedings, pages 59-72, 1992.

[2] W. B. Croft and P. Savino. Implementing Ranking

Strategies Using Text Signatures. ACM Transactions on Information Systems, 6(l):42-62, 1988.

[3] U. Glavitsch and P. Schauble. A System for Retrieving Speech Documents. In N. Belkin, P. In- gwersen, and A. M. Pejtersen, editors, ACM SI- GIR Conference on R&D in Information Retrieval,

pages 168-176, 1992.

[4] D. Harman. Relevance Feedback Revisited. In N. Belkin, P. Ingwersen, and A. M. Pejtersen, ed168
itors, ACM SIGIR Conference on R&D in Infor- mation Retrieval, pages 1-10, 1992.

[5] D. Harman, E. Fox, R. A. Baeza-Yates, and

W. Lee. Inverted Files. In W. B. Frakes and R. A. Baeza-Yates, editors. Information Retrieval, Data

Structures & Algorithms, pages 28-43. PrenticeHall, Englewood Cliffs, NJ, 1992.

[6] M. F. Porter. An Algorithm for Suffix Stripping.

Program, 14(3): 130-137, 1980.

[7] J. A. Rice. Mathematical Statistics and Data Analysis. Wadsworth & Brooks, Pacific Grove, CA,

1988.

[8] G. Salton and C. Buckley. Parallel Text Search

Methods. Communications of the ACM, 31(2):202-

215, 1988.

[9] G. Salton and C. Buckley. Improving Retrieval Per- formance by Relevance Feedback. Journal of the ASIS, 41(4):288-297, 1990.

[10] G. Salton and M. McGill. Introduction to Modern

Information Retrieval. McGraw-Hill, New York,

1983.

[11] P. Schauble. SPIDER: A Multiuser Information

Retrieval System for Semistructured and Dynamic

Data. In ACM SIGIR Conference on R&D in In- formation Retrieval, pages 318-327, 1993.

[12] C. J, van Rijsbergen. Information Retrieval. Butterworths, London, second edition, 1979.

169
1. Exact and Approximate Retrieval Status Values

"exact" : RSV{q,dj) := * aij*bi

"approx" : RSV'>{q,dj) -= jir- * E < *

2. Document Length

3. Weights of Query Features

V Vi^dj

"ntn" : bi := ff{<Pi,q)*nidf{(pi)

"ltn":bi := {1 + log{ff{<pi,q))) * nidf{>pi)

4. Exact and Approximate Weights of Document Features

,0

"/nc" : Oij

'He" : (Hj

,0

max{//(yjfc,d,)|y)fc€d,}

1 * md/(y>j)

(l + /og(//(yi,d,)))

(1 + log{msLx{ff{<ph,dj) | £ dy}))

1

(i + M//(y>».^;)))

(1 + log{max{ff{(ph,dj) \ <ph 6 dj}))

1 * nidf{(pi)

* nidf{(pi)

5. Normalized Inverse Document Frequency

niifM := l- '°flog(l + n)

Table 1: Classification of weighting schemes.

170
N-Gram-Based Text Filtering For TREC-2

William B. Cavnar

Environmental Research Institute of Michigan

P.O. Box 134001

Ann Arbor, MI 48113-4001

cavnar@erim.org

Abstract

Most text retrieval and filtering systems depend heavily on

the accuracy of the text they process. In other words, the various mechanisms that they use depend on every word in the text and in the queries being correctly and completely

spelled. To get around this limitation, our experimental text

filtering system uses N-gram-based matching for document

retrieval and routing tasks. The system's first application was for the TREC-2 retrieval and routing task. Its perfor- mance on this task was promising, pointing the way for several types of enhancements, both for speed and

effectiveness

.

1.0 Background

Even though modem character recognition techniques are steadUy improving, scanning and recognizing characters in paper documents stiU results in many errors. These errors can impact the ability of a standard, classical text retrieval system to process the resulting ASCII text by interfering

with word stemming, stopword identification, or even basic

indexing. Furthermore, as paper documents age, their print

quality can degrade, possibly causing further recognition

problems. This is becoming a serious problem, especially

given the vast amount of material that exists in a paper form

that has a limited Ufetime.

Another difficulty is that the texts themselves may contain

spelling variations (e.g., British vs. American) or outright

speUing errors. The same is true of the queries entered by a human user. Discrepancies between the spelling of a word

in a document and a word in a query may prevent a match.

A third problem area is that current word stemming and

stopword list construction algorithms sometimes involve a

significant amount of knowledge about the documents' lan- guage. Although there are automatic approaches to acquiring this kind of knowledge, it is complicated [1]. An ideal

text retrieval method would require no special language

knowledge, and in fact would be able to handle multiple

languages (represented, say, in Unicode) simultaneously.

AU of these problems have analogs in a somewhat different problem domain, namely, reading and interpreting postal addresses on pieces of mail. For the last eight years, the Environmental Research Institute of Michigan (ERIM) has

conducted research for the United States Postal Service

(USPS) in automatic address interpretation. The ultimate

goal of this research is to build a high-performance address

interpretation unit that can reUably and efficiendy read the address information on a mailpiece, and determine its correct 9-digit or 11 -digit ZIP code, even if that code is not

present on the mailpiece or is in error.

There are two parts to interpreting an address from a mailpiece, and both of them are difficult:

• Recognizing the characters in the address block. This is difficult because of poor print quaUty, poor contrast, complicated backgrounds, presence of logos and nonaddress information, use of proportional fonts, and a host of other problems.

• Interpreting the lines of the address. This is difficult because of spelling errors, addressing errors, unusual

abbreviations, strange local addressing conventions, and

errors and omissions in the USPS databases.

Our current address interpretation systems use a number of

different techniques to get around these problems. Among

these is the use of N-gram-based matching to find matches

in postal databases. (Please see [2] and [3] for details of

these systems.)

In this work, N-grams are N-character slices of some longer

string. We do not use positional N-grams; i.e., what is important is whether a given string contains a particular

N-gram, not where it falls in the string. We use bi-grams

(N=2) and tri-grams (N=3) together in the same system.

Bi-grams and tri-grams complement each other in the sense

that bi-grams provide somewhat better matching for indi- vidual words, while tri-grams provide the connections

between words to improve phrase matching.

171
N-gram-based matching addresses the difficulties mentioned above for postal addresses in ways that have application for document retrieval:

• It usually finds good matches for postal addresses even

in the face of a significant number of individual character recognition errors. This is particularly true if the sys- tem can use redundant data for the search key, such as the city, state, and ZIP together. This is identical to the problem of matching document text that was scanned

from poor quality images. Words that occur near one

another in a text also "reinforce" each other for search- ing the way that city, state, and ZIP do.

• It easily overcomes problems with spelling and address- ing variations and errors on mailpieces or in postal data- bases. This is analogous to the problem of matching

document text that contains spelling variations. It also achieves an effect similar to stemming in that different words having the same root are considered to be similar.

• It can also handle abbreviations fairly easily. This is also similar to the problem of word stemming.

• Some postal address interpretation systems depend

heavily on the system's ability to recognize certain key

words, such as STREET or BOX. These words play a

role similar to stopwords in that they carry little content, and mostly provide structural information. N-grambased matching can handle errors even in these words.

Drawing on the experience of using N-grams in postal

work, ERIM has been adapting these techniques for use in full-text retrieval and filtering. We currently have a working

full-text retrieval system, caUed zview, which provides a simple query faciUty for accessing a wide variety of ASCII

documents, including biographies, abstracts, and documentation. The zview system's N-gram-based matching provides a very simple, easy-to-use text retrieval system. It is tolerant of spelling variations and errors in both the text and the doc- uments. It also provides a straightforward ranking of documents that enables the user to see which documents best match the query. This system has given us some confidence

in the wider utility of N-gram-based matching for information retrieval. Although the TREC task would not seem to directiy require inexact matching (the documents were high

quality ASCII), our experience with zview indicates that this kind of tolerance for variations in user input is very useful

for general retrieval.

Incidentally, one criticism that one can make of N-grambased systems is that they require large amounts of storage.

Although this is true of the naive implementation, there are a number of variations using various superimposed coding

techniques that provide substantial space savings without

impacting matching performance. See [2] for details.

2.0 System Description

The zview system mentioned above has two parts: an offline index builder, and an on-line retrieval interface. In its current form, zview does not yet use our most compact

N-gram representation techniques, and is thus not wellsuited for very large (i.e., gigabyte size) databases. Also, it can handle only single query strings, not the multiple query

strings necessary for TREC. Instead, for this task we opted

to use a simple filter architecture, and concentrated on making it as fast as possible. This architecture has several advantages:

• It requires no disk space for an index. In fact, we were

able to save even more disk space by leaving the original

documents in their compressed fonn, and uncompressing them on the fly.

• It is conceptually simple, requiring only a very modest

amount of code (987 lines of C source). The whole sys- tem took only a few days to design and build.

This architecture is similar in concept to that used by Mark

Zimmerman's PARA system, which participated in last year's TREC. That system used a set of handcrafted Unix

awk scripts to filter the documents. The PARA system,

although it got good results , took 22 days on a more or less dedicated NeXT machine, even though its match criteria generally specified fewer match terms than our system did. Also Zimmerman's system used the notion of proximity to identify sets of matching lines that occurred near one

another, whereas our system counts matches throughout the document without regard to the proximity of match terms. See [4] for details.

Obviously, a significant disadvantage of such a filter-type architecture is that the system has to read all of the data to simulate a retrieval. This is very computationally intensive,

but we were able to mitigate this somewhat in our system by

handling much of the actual N-gram-based matching with a

highly parallel bit-vector approach, which we describe

below. Also, we were able to split the processing load up

among a suite of computers and run them all in a coarse- grained parallel fashion.

Figure 1 gives an overview of our system in dataflow fashion. We discuss each of the processes in this diagram in the following sections.

172
Compressed

Documents

I

Queries Uncompressed

Documents

Relevance Scores

Top Choice Scores

Figure 1: Dataflow Diagram for N-Gram-Based Text

Retrieval

2.1 Generating Query Strings From TREC

Topics

The process labelled "Generate Queries" represents a pro- gram gen_query, which takes the TREC topics file and generates a set of query strings for each topic. We considered

several different schemes for extracting query strings from

the topics, but finally settled on using just the phrases in the concept and nationality sections. The following is a typical

topic from TREC- 1: <num> Number 007

<title> Topic: U.S. Budget Deficit <desc> Description: Document will mention a proposal to decrease the U.S. budget deficit. ...<con> Concept(s):

1 . U.S. budget deficit, federal budget shortfall

2. foreign affairs budget, defense budget, en- titlements

3. increased revenues, tax increase, tax re- form, auction quota

4. reduction in expenditures, spending cuts,

cutting domestic

programs, eliminating government subsidies

5. NOT financing the U.S. budget deficit

<nat> Nationality: U.S.

Given this topic, the gen_query program generates the fol- lowing set of query strings:

007 000 U.S. 007 000 U.S. budget deficit 007 001 federal budget shortfall 007 002 foreign affairs budget

007 003 defense budget

007 004 entitlements

007 005 increased revenues 007 006 tax increase

007 007 tax reform

007 008 auction quota

007 009 reduction in expenditures

007 010 spending cuts 007 01 1 cutting domestic programs

007 01 2 eliminating government subsidies

007 013 NOT financing the U.S. budget deficit

173
Each line of the query output corresponds to one nationality or concept string from the topic. The nationality strings are

listed first, followed by the concept strings in the order they

appeared. There is also a rank number associated with each

query string. Generally, as concept strings were used in the

topics, strings that appeared earlier in the list were more

important than strings that appeared later. The actual filter- ing program also uses these rank numbers to weight the query strings in importance. Each string receives a weight

equal to 2k - r, where k is the number of query strings for the topic and r is the string's rank value. Any nationality or concept string can also have a NOT prefix, which triggers a

slightly different kind of processing for these strings,

described below.

2.2 The Problem of Finding Matches While

Filtering Text

In Figure 1 above, the process labelled "zcat" represents a Unix utility program that takes a compressed file and

returns its uncompressed form. The process labelled "Find

Matches" represents a programfirui_matches , which takes a

set of queries for all of the topics at once, and applies them

to a single uncompressed text file. Given the filter architec- ture for this system, it was important for us to minimize the

total amount of I/O. By letting find_matches process the queries for all topics simultaneously, the system has to pass

the data only once.

To make this process as efficient as possible, _^J_mafc/ie5

does most of its matching using bit-vectors representing the presence or absence of particular N-grams. Suppose that we

had a string that we wanted to search for in a document

using N-grams. An obvious way to do it would be to per- form the following steps:

• Break the query string up into N-grams.

• For every line in the document, count how many of

those N-grams occurred in the line. This count would be

the match score for that line.

• Keep a sorted Hst of the top-scoring Unes, bumping the lowest scoring entry on this list when a new line has a

better score.

When this algorithm finishes, we will have a list of the top

scoring matches for our query string. We could make this process a little more sophisticated by putting a threshold of some sort on it. If a line's match score was below this threshold, say 80% of the number of N-grams in the query

string, we could ignore this line as simply not containing

enough of the query string to consider.

Unfortunately, this naive implementation of N-gram-based

matching, which uses nested loops to compare every N-gram in the query string with every N-gram in a line, is computationally very costly.

2.3 Matching and Scoring Lines

We can do significantly better than the naive N-gram-based

algorithm described above by using hashing. Figure 2 illus- trates this process for bi-grams. The key idea here is to break the matching process into two parts:

• Initialize a hash-code-based look-up table for all of the N-grams in the query string. Each slot simply records whether any N-gram hashed to that position (1) or not

(0). Thus the hash table is a simple bit vector.

• For each line in the document, make a single pass for aU

of the line's N-grams, checking each one to see if it is in the table. In other words, see if the slot (bit) corresponding to the N-gram contains a 1. If it does, add 1 to the

line's score.

If we make the hash table big enough, we can make the

probability of collisions arbitrarily small. For example, consider that there are only 52,022 possible bi-grams and tri- grams for the alphabet consisting of A-Z, 0-9, and space. A

hash table size of, say, 20,000 slots (625 32-bit words or 2500 bytes) provides far more than ample space to avoid

collisions. If there is a collision between the hash of an N-gram in the query string and that of some different N-gram in the line, its only effect is to cause that line's score

to be one higher than it should be. It is even more unlikely

that two or more of the N-grams would also collide. Thus by

allowing collisions in the hash table, we get a simple, quick

algorithm at the cost of a small probability of small distor- tions in the line scores. See [2] for a further discussion of

how to estimate the probability of these kinds of collisions. Note that we include leading and trailing spaces for words

in counting N-grams. Also recall that in our full implementation of this we process tri-grams the same way and at tlie same time. The match score between two strings includes

the count of both matching bi-grams and matching tri- grams.

To get an additional gain in efficiency, we can carry this hash table scheme for counting N-grams another step by

using parallelism to match a single line from a document

against a number of query strings all at the same time. Fig- ure 3 illustrates this scheme. Again, matching is a two-step

process:

174
STRING

S,ST,TR,RI, IN,NG,G Bi-grams

hash

function

Hash

Table

Bit

Vector

hash

function

_S, SP, PR, RP, UN, NG, G_

Boxes indicate matches

between N-grams from

the two strings. The

match score for these

two strings is 3. Bi-grams

SPRUNG

Figure 2: Using Bi-Grams to Compare "STRING" and "SPRUNG"

• Initialize a hash table for the entire set of query strings.

Each slot in the hash table again represents one N-gram,

but it is now a bit vector in which the kth bit indicates whether the N-gram occurs in the kth query string.

• For every line in the document, hash each N-gram to find the corresponding bit-vector. Then add that bit vector to the current score vector for the document. The kth element of this score vector keeps the current match

score for the document against the kth query string.

One other significant aspect of this multi-query-string

implementation is that we also represent the final score vector for all of the queries as a set of parallel bit-vectors. This

effectively allows us to perform 32 additions in parallel by

adding one 32-bit word of an N-gram bit-vector from the

table to one 32-bit word representing the 1-bits of 32 query

string scores, and then propagate the carry bits up to bit-vectors representing the higher order bits of the scores. Effectively, this lets us compute scores for 32 query strings in close to the same amount of time as it takes to compute the score for a single query string.

2.4 Scoring and Ranking Documents

After the system computes the N-gram match scores of a

single Une against all of the query strings, it applies a

threshold criterion. If the score for a particular query string

is below its threshold, the system treats it as a zero score. The threshold is defined as a percentage of the maximum

possible N-gram score for the query string. For example, if a particular query string has a maximum possible score of

20 matching N-grams, and the threshold percentage (as determined by a command line argument to the program) is 70%, then the system will ignore any line whose score is less than 14. There is a similar command Une argument

which determines the threshold percentage for negation

query strings (those tagged with a NOT prefix in the query

string set). If a document has a line whose score for a negation query string exceeds its threshold, the system will dis- card the document. For most runs of the system, we got the

best results with a match threshold at 70% and a negation

threshold at 95%. In other words, we were willing to accept a line as a match for a given query string if it had 70% of the

175
Document

Line

"expansion of Medicare to cc ver catastrophic illnesses, faster"

Hashed N-gram

Indices

hash

function

Query String Indices

Bit-vector for kth query string

Bits for score of

kth query

string

Accumulated

parallel score

bit- vectors for hashed N-grams

over all query

strings, up to and including "ve" bi-gram

8 4 2 1

Bit-vector for presence of a

particular N-gram

in all query strings

Add selected

bit- vector to

I's score vector, and propagate

carry bits up Figure 3: Parallel Implementation for Multiple Query Strings

requisite N-grams, but needed a nearly exact match in order

to discard it for matching a negation query.

To compute the scores for a whole document, we accumulated the line scores for each query string, but applied a cap

at twice the maximum possible N-gram score. In other words, if a document mentions a particular query string

twice, that is twice as good as mentioning it once. However

mentioning it three or more times is of no additional value. We put this cap into the system after observing that initial versions of the system would overrate some documents

because they mentioned some low ranking query string for a given topic many times, but completely missed any of the higher ranking query strings for the topic.

To generate the final scores for a document against a topic, the system accumulates the scores for each query string in the topic, multiplying them by the weighting factor computed earlier. If this aggregate weighted score exceeds a hard-coded threshold of 40, the system then writes out this score as a relevance judgement for the document. Later a

separate program reads the file containing the relevance

scores, computes the top 1000 scores for each topic, and

writes them out as the final system result.

3.0 Multi-Processor Execution

Even with all of this attention to efficient implementation,

the system stiU required a large amount of computation.

Fortunately, ERIM had available a network of Sun workstations over which we could distribute the processing load. This network consisted of two Sun SPARCstations and five or six Sun SPARCstation 2 machines. We used a very simple partitioning scheme, assigning each machine a fixed set of documents to evaluate against aU of the topics. Given this arrangement, a full run of topic set 3 against Disks 1 and 2

took 12 to 13 hours if aU of tiie machines were fully avail- able. When there was significant contention on some of the machines, the run might take as much as. 24 hours. An obvi- ous drawback with using the fixed partitioning scheme was

that often one or more of the machines would not finish

until many hours after all of the others had. This was usually

due to unexpected heavy contention on one or more

machines. If we had used a more intelligent and dynamic

document file assignment mechanism (implemented in, say, the Linda coordination language), we would have had better elapsed execution times.

176
TABLE 1. Selected TREC-2 Test Results

Test Number Note Test Type

Query

Thresh.

Negation

Thresh.

Average

Precision At 10 Docs

At 100

Docs

RPrecision

erimal official ad hoc 80 80 0.1589 0.3960 0.3016 0.2179

erima2 official ad hoc 75 80 0.1885 0.4480 0.3426 0.2494

a30 official ad hoc 70 95 0.1604 0.4040 0.3038 0.2198

a31 official w/

X queries

ad hoc 70 95 0.1153 0.4000 0.259 0.1904

a33 spanning

lines

ad hoc 70 95 0.0734 0.15 0.1484 0.1333

a34 span lines; exp. decay

weighting

ad hoc 90 95 0.1099 0.314 0.2276 0.1776

a35 span lines; exp. decay

weighting

ad hoc 80 95 0.1336 0.3000 0.2442 0.2004

a35-AP APonly ad hoc 80 95 0.2717 0.4740 0.2838 0.3140

a36 span lines; exp. decay

weighting

ad hoc 70 95 0.0792 0.1780 0.1678 0.1387

erimrl official routing 80 80 0.1219 0.3580 0.2304 0.1814

erimr2 official routing 75 80 0.1415 0.4240 0.2524 0.2031

r5 official routing 70 95 0.1225 0.3600 0.2310 0.1818

i6 span lines; exp. decay

weighting

routing 80 95 0.1015 0.2880 0.1954 0.1604

r7 span lines; exp. decay

weighting

routing 70 95 0.0602 0.2200 0.1388 0.1123

4.0 Results

To test our system, we ran it a number of times, varying dif- ferent system parameters. After the conference, we were

also able to make a few changes and run it again. Table 1 above summarizes some highlights of our results, which

show a number of interesting points:

• The tests numbered erimal, erima2, erimrl and erimr2 were the official results turned in on June 1 . The first two were the ad hoc results, and the second two were the routing results.

• The tests numbered a30, a35, a36, r5, and r7 were aU

attempts to determine good settings for the query thresh- old and negation threshold parameters. Unfortunately,

the results from these test runs simply do not provide

anywhere near enough data to perform a complete sensitivity analysis for these parameters. Also, we noticed in some of our testing on the TREC-1 queries that there is

considerable difference in the optimum values of these

thresholds for different topic sets/data set combinations.

One of the motivations for using N-gram-based matching was that it provides good matching performance in the face of textual errors. To test this idea, we ran test a31 using dehberately damaged query strings. In this

test, we took each query string produced by gen_query,

and replaced the third character with the letter "X". This

works out to be an effective character recognition error

rate of 4.5% over the whole body of query strings.

Although the system took a considerable hit in perfor- mance, the interesting thing was that it still functioned at

all. Many of the other systems in the TREC evaluation

would most likely have completely failed in an analo- gous test, since they depend heavily on exact word

matches.

One serious drawback to the original system was that it did not span hues when matching. That is , if the text that

177
a particular query should match was spht across two

document lines, then the match would fail. After the conference, we found a relatively easy way to rectify this design deficiency and were able to run tests a33 though

a36, and r6 and r7. Unfortunately, this change greatly affected the system's response to the query and negation

thresholds, and we were not able to run enough tests to find the optimum values for these parameters. The end

result is that the best results we have for this improved

system are still not as good as the official results we

reported.

• Another serious problem with the original system came

to light during the official conference. As Donna Harman

pointed out in her closing talk, no one has yet really

explored the full ramifications of changes in term

weighting strategies. Our original system used a very simple-minded linear-faUoff weighting scheme. Our

assumption was that concept strings appeared in reverse order of importance. However, we began to suspect,

based on different concepts that were mentioned in a number of the conference presentations, that this was

overly simplistic. We decided to implement a straightforward exponential-decay weighting scheme. In this approach, the first query string gets a weight of 100, the second gets a weight of, say, 90, the third a weight of 81

,

the fourth a weight of 72, and so on, with each succeeding weight taking 90% of its predecessor's value. Unfortunately, we did not have time to tune the system's response to this change either, and its results (a34, a35,

a36, r6, and r7) are worse than the official results as

well. However, it appears that there is plenty of room for experimentation with different term weighting schemes

and we will continue working with them.

• In our early work with the system before sending in the

official results, we did a fair amount of testing using just the Associated Press document set. We were perhaps

misled by how well the system did on these documents,

and missed some chances to improve the system earlier. The test labelled a35-AP shows what the results look

like for a35 if we restrict the new system to just return- ing AP documents, and restrict the relevance judgements

to just AP documents. Even with this imperfectly tuned new version, we see that the system is capable of significantly better performance. It is unclear why there should

be such variation between the retrievability of the AP

documents and the other document collections.

At its best, our system performed as well as most of the sys- tems that participated in TREC-1. However, there is ample room for improvement, as we have noted above, especially

in comparison to many of the systems that came back for TREC-2.

5.0 Further Research

The TREC-2 task is the first real application for our N-gram-based multiple-query system. As in any experiment

of this nature, the results and problems suggest many more

possible avenues of research. These ideas fall into two categories.

5.1 Analyzing the Current System's

Performance

Further analysis of the existing system will allow us to better understand its behavior and limitations. Some ways to do

that include:

• It is likely that generating query strings from the topic

concept strings may have significantly limited perfor- mance. For example. Topic 74 about instances where ttie U.S. government propounds conflicting policies completely failed to mention terms such as policy or regulation in the concept Ust. Thus, our system had only a very

small chance whatsoever of finding matching documents. Zimmerman's filtering system [4] did well with

handcrafted queries, so we should also try manually generated queries.

• Currently the system has a hard-coded cutoff threshold of 40 for the weighted aggregate score. The purpose of

the threshold was to prevent the system from returning

results that were guaranteed to be noise because of then- very low score. This value was set more or less arbitrarily, so we should experiment with changing this threshold to determine its true effect. In all likelihood, it could be a fair amount higher, preventing the system

fi-om generating other useless low-scoring results.

• Currently the system sets a cap of three times the maximum N-gram score for any query string score. Again,

this value was determined only by a very rough empirical process, so we should experiment with changing this cap, to see how much impact it has.

5.2 Extending the System

We can also make some significant changes to the system to explore possibilities for other performance improvements.

• Currently the system treats upper and lower case alike

for both documents and queries. Since acronyms and

brand names have different meanings sometimes from

uncapitaUzed words having the same letters, perhaps

there is a way to take the case of letters into account when computing a match. That is, we could count a

178
match between two words with different capitaUzations

as a good match, but a match between two words with

the same capitalizations as a better one.

• Our system is basically just a text filter. As such, it is already close to being useful for various routing tasks. However, to use N-gram-based matching for on-line

retrieval, we will have to implement a true index-based

system. Ideally, we would Uke to integrate the text retrieval capability of our ryiew system with the flexibil- ity of the multi-query filtering system described here. Although we have an initial design sketched out, it will take an investment of further time and machine

resources to implement this idea and test it. We will also be using some of our new compact N-gram representation techniques to reduce the large amount of index stor- age and computation required.

Acknowledgments

We would like to thank our sponsors at DARPA/SISTO for allowing us the opportunity to participate in TREC-2. We

would also Uke to thank Donna Harman and her staff at NIST for all of their help. Finally, we are grateful for the United States Postal Service's sponsorship of the original

N-gram-based matching technology that inspired our TREC

research.

References

[1] W. B. Frakes. Stemming Algorithms. In WiUiam 6. Frakes and Ricardo Baeza-Yates, Editors. Information

Retrieval: Data Structures & Algorithms, pages 131-

160. Prentice Hall, Inc. Englewood Cliffs, NJ, 1992.

[2] WilUam B. Cavnar and Alan J.Vayda. Using superimposed coding of N-gram Usts for efficient inexact

matching. In Proceedings of the Fifth USPS Advanced

Technology Conference, pages 253-267, Washington,

DC, 1992.

[3] William B. Cavnar and Alan J.Vayda. N-gram-based

matching for multi-field database access in postal appli- cations. In Proceedings of the 1993 Symposium On

Document Analysis and Information Retrieval, pages

287-297, University of Nevada, Las Vegas.

[4] Mark Zimmerman. Proximity-correlation for document

ranking: The PARA Group's TREC Experiment. In Proceedings of the First Text REtrieval Conference

(TREC-1), NIST Special PubUcation 500-207 , 1992.

179

Retrieval of Partial Documents

Alistair Moffat* Ron Sacks-Davis^ Ross Wilkinson* Justin Zobel^

Information systems usually retrieve whole doc- uments as answers to queries. However, it may

in some circumstances be more appropriate to re- trieve parts of documents. These parts could be formed by arbitrary division of running text into pieces of similar length, or by considering the doc- ument's hierarchical structure. Here we consider

how to break documents into parts, how to implement retrieval of parts, and the impact of division

of documents on retrieval effectiveness.

1 Introduction

Provision of answers to informally phrased questions is a central part of information retrieval. These answers traditionally take the form of documents retrieved from a text database, but documents will often be unsatisfactory as answers. They may be large and unwieldy; the answer they

represent may be diffuse, and therefore hard for the user to extract; and word-based retrieval sys- tems may be misled by the breadth of vocabulary of a long document into believing it to be

relevant. Indexing and returning parts of documents addresses these problems. We have approached the

problem of partial documents in two ways. The

first approach is to regard documents as an unstructured series of "pages" of text of similar

length, each of which can be returned as an an- swer to a query. We would expect, under this approach, that any bias in the retrieval mechanism towards documents of a particular length

should be eliminated. By regarding an answer to be the document from which an answer page is drawn, paging can be used even in contexts where

*Dept. of Computer Science, The University of Mel- bourne, Parkville, Victoria, Australia 3052;

alistair®cs.mu.oz.au

* Collaborative Information Technology Research Insti- tute, 723 Sw£uiston St., Carlton, Victoria, Australia 3052;

rsd®kbs.citri.edu.au Dept. of Computer Science, EMIT, GPO Box 2476V,

Melbourne, Victoria, Australia 3001; ross@c8.rmit.oz.au §Dept. of Computer Science, EMIT, GPO Box 2476V,

Melbourne, Victoria, AustreJia 3001; jz®cs.miit.oz.au

documents are required as answers, as is the case

for the TREC experiments.

Breaking documents into pages, however, has

implications for implementation: the growth in the number of candidate answers is such that

current approaches for evaluating queries have

unacceptable memory requirements and response

times. We have developed new algorithms for implementing information retrieval methods on

large collections, concentrating on the cosine measure with IDF term weights as a typical ex- ample. These include techniques for efficiently

constructing and compressing large inverted files, and for restricting the amount of memory space

and processing time required during query eval- uation. The result is that we are able to identify answers in a fraction of the space and time

required by previous methods. Using these tech- niques on a version of the TREC data in which

the documents are broken into over 1.7 million

pages, answers can be found more quickly than

they could previously be found on the unpaged

data, even though the latter has a smaller index

and fewer records. Section 2 gives an overview of

these techniques.

Our second approach to the problem of partial documents was to regard documents as hi- erarchical structures. Some of the documents in TREC are very large. It is not clear that it is desirable to return such documents as a whole, nor is it clear that these documents should be in- dexed as a whole. Most of the long documents

in TREC are from the Federal Register collection

and all have some degree of structure associated

with them. We conducted a set of experiments

that attempted to determine whether these documents should be indexed as single objects, and

whether the documents' structure could be used

in conjunction with the contents of its elements.

We also experimented with retrieval of partial documents and investigated whether context,

that is the rank of the whole document, helped

improve ranking of sections. The experiments

with hierarchical structures are necessarily based on the small set of longer documents in the TREC

181
1. Set prev <— undefined and curr <— wi.

2. For each j from 2 to n,

(a) If curr > B, emit prev if it is defined,

set prev «~ curr, and set curr <—

(b) Otherwise, if Wj > prev then set preu <— prev + curr and set curr <— tyj.

(c) Otherwise, set curr curr + Wj.

3. If curr < B then set prew <— prev + curr and

emit prev. Otherwise, emit prev followed by

curr.

collection. We would expect that, in a large

database of such documents, the implementation

techniques we developed for unstructured text could be used for structured documents almost

without change.

2 Retrieval of paged text

The first issue that must be considered when ac- cessing a set of documents as a collection of pages

is the pagination strategy; one possible method

for pagination is discussed in Section 2.1. Then, given paged text, answers must be found

in response to documents. In an inverted file text database system of the kind we have been devel- oping [6, 9], the implications of paging are a large

increase both in the number of accumulators used

to hold the intermediate cosine values and in the

length of the inverted file entries. The former means that evaluating a ranking will require more memory; while the latter implies a longer time to resolve queries. Methods for avoiding these in- creases are outlined in Sections 2.2 and 2.3; a full description is given elsewhere [10]. Finally, there is the impact on effectiveness of the decision to paginate the documents. Experimental results comparing document retrieval with page retrieval are described in Section 2.4.

2.1 Breaking text into pages

One way to store text in a database is as a set of records, each containing a single document.

Such an organisation implies that entire documents must be retrieved in response to queries.

Moreover, a query can match long documents in which its words are widely separated and could

well be unrelated, so this storage strategy can re- sult in irrelevant material being retrieved. An alternative way of presenting text is as a

series of pages. There are several advantages to using pages:

• they are of a more manageable size than

whole documents, and the size variation be- tween pages can be constrained to be far less than the size variation between documents;

• if a user looks for answers matching a query,

it is likely that in relevant material the

query's words will occur close together in the

retrieved text;

• retrieving a page of text to display may be

considerably cheaper than retrieving an entire document;

Figure 1: Paging algorithm

• in many applications it is natural to regard

documents as consisting of parts rather than

as a whole; for example, the nodes in a hypertext system or the sections of a book; and

• only parts of documents can, in general, fit on a screen, and people can only comprehend

part of a document at a time.

For these reasons—and because of the challenge

posed by the sheer size of a paged version of the

TREC collection—experiments were undertaken

with paged text. The paging strategy adopted first breaks documents into minimal units likely to be useful for ranking. In most collections this unit would probably be the paragraph. The algorithm shown in Figure 1 is then used to gather paragraphs into pages, where Wi is the length of the i'th para- graph of the original document and B is the tar- get length of each constructed page. Documents

of fewer than B bytes must, of course, be allo- cated singleton pages, but all other pages are B

bytes or more. Moreover, the aim is that only

a small number of pages are significantly longer

than B bytes. The algorithm requires time linear

in the length of the text and a constant amount

of space, and so is a small additional step during

database construction. For the experiments de- scribed below, length was measured in bytes (the

alternatives being words, or some more abstract

length value based upon term frequencies), and

B = 1,000 was used. The pagination of the 2,055 Mb TREC collec- tion resulted in the 742,358 documents being con- verted into 1,743,848 pages of average size 1,235

bytes each.

182
1. Order the words in the query from highest

weight to lowest.

2. Set A *— 9; A is the current set of accumula3. For ezLch word t in the query,

(a) Retrieve /t, the inverted file entry for t.

(b) For each {d,fd,t) pair in It,

i. If the accumulator Ad A, calculate Ad ^ Ad + Wq^t Wd.tii. Otherwise, set A <— yl + {y4ci}, calr'lilat** A J 4 III ^ • m J ^

(c) 11 \A\ > L, go to step 4.

4. For each document d such that Ad € A, cal- culate Cd <— Ad/IVd.

5. Identify the r highest values of Cd using a

heap.

2.2 Management of accumulators

An effective vpay to rank documents against a

query is with the cosine measure, defined by the

function

where q is the query, d is the document, and w^^t

is the weight of word t in document or query x. We assume that Wd = VilZt '^dt) ^^e length

of document d, and that the top r answers are to be retrieved and returned to the user. In our experiments term weights were calcu- lated using the IDF rule wa^t = fd,t ^og{N/ft),

where fd,t is the number of appearances of term

t in document d, N is the number of documents

in the collection, and ft is the number of documents that contain term t. Harman [3] gives a summary of ranking techniques and a discussion

of the cosine measure and IDF weighting rule. To support the cosine measure in an inverted

file text database system, each inverted file entry

contains a sequence of {d, fd,t) pairs for some term

t. The value ft, the number of documents containing t, is stored in the lexicon, and from these

two the cosine contribution fd^t (log(-^//<))^ °f

term t in document d can be calculated. A struc- ture of accumulators is used to collect these contributions. As the inverted file entry for each

query term is processed, the accumulator value

for each document number in the inverted file entry is updated by adding in each partial co- sine value as it is calculated, so that by the time

all inverted file entries have been processed the

accumulator for document d contains the value

fd,t • (iog(i\^//t))' = ^,,t

t t The number of accumulators required to process a query can be large. Queries were created

from TREC topics 51-100 by simply extracting

and stemming all of the alphanumeric strings,

and stopping 601 closed-class words. The gen- erated queries had an average of over 40 terms,

and each resulted in about 75% of the pages

of TREC having a non-zero accumulator value. With this ratio the most memory-efficient accu- mulator structure is an array. But at 32 or 64

bits per accumulator, this structure is formidably

large, and dominates the retrieval-time memory requirements. Even initialising it is timeconsuming.

Figure 2: QuU algorithm for computing cosine

using approximately L accumulators

A simple strategy for restricting the number of

accumulators is to order query terms by decreasing weight, and only process terms until some

designated termination condition is met. One

such condition is to impose an a priori bound L

on the number of non-zero accumulators, and to stop processing terms when this bound is reached.

Such a ranking process, which we call quit, is il- lustrated in Figure 2. Other possible termination

conditions would be to place a limit on the number of terms considered or on the total number

of pointers decoded; or to place an upper bound

on the term frequency ft , and only process terms

that appear in fewer than x% of the documents,

for some predetermined value x. Buckley and Le- wit [1]; Lucarella [8]; and Wong and Lee [13] have

also considered various stopping rules, and derive

conditions under which inverted file entries can be

discarded without affecting the r top documents

(although their order within the ranking may be

different). Here we are prepared to allow approximate as well as exact rules, acknowledging that

the cosine measure is itself a heuristic. Quitting has the advantage of only process- ing the inverted file entries of a subset of the

query terms, and hence of faster ranking, but

at the possible expense of poor retrieval performance, depending upon how discriminating the

low weighted terms are. An alternative is to continue the processing of

183
1. Order the words in the query from highest

weight to lowest.

2. Set A<^9.

3. For each word t in the query,

(a) Retrieve It-

(b) For each {d, fd,t) pair in It,

i. If Ad G A, calculate Ad Ad +

Wq,t Wd,tii. Otherwise, set A «— A + {Ad}, cal- culate Ad <— Wq^t Wd,t-

(c) If \A\ > L, go to step 4.

4. For each remaining word t in the query,

(a) Retrieve It.

(b) For each d such that Ad € A,

if {d,fd,t) € It, calculate Ad ^ Ad +

Wq,t Wd,t5. For each document d such that Ad G A, cal- culate Cd <— Ad/Wd.

6. Identify the r highest values of CdFigure 3: Continue algorithm for using approximately L accumulators

inverted file entries after the bound on the number of accumulators is reached, but allovi^ no new

documents into the accumulator set. This con- tinue algorithm is illustrated in Figure 3. The

algorithm has two distinct phases. In the first phase, accumulators are added freely, as in the

quit algorithm. In the second stage, existing ac- cumulator values are updated but no new accu- mulators are added. Both quit and continue generate the same set of approximately L candidate

answers, but in a different permutation, so when

the top r documents are extracted from this set and returned, different retrieval effectiveness can be expected, particularly if r <C i^. A similar strategy to that of continue is de- scribed by Harman and Candela [3, 4], although

their motivation is somewhat different—they desire a small number of accumulators to reduce

the sorting time required for a total ranking of

the collection, whereas we assume that only a small fraction of the collection is to be presented. In this latter case, to find the top r documents

a heap is the appropriate data structure, and

0{N + rlogN) time is required, a small fraction

of query processing time.

Figure 4 shows retrieval effectiveness, measured as an llpt average precision, as a function

of k =: \A\, the number of accumulators actually

used. The small numbers beside the continue curve show the average number of terms processed in phase one of each query. For example,

only 8.2 terms are needed to generate 27,000 ac- cumulators. The difference between quit and continue is marked, and, perhaps surprisingly, even

the mid to low weight terms appear to contribute

to the effectiveness of the cosine rule. Ignoring

them leads to significantly poorer retrieval.

20 -I

co

'uo

0)Q.

I "5o

15 -

10 -

42.4

continue

quit

-1 1 I I I M I 1

I

' 1000 10000 100000 1000000 Number of accumulators

Figure 4: Effectiveness of quit and continue

In these experiments direct assessment of the

relevance of pages was not possible because the

relevance judgements were for whole documents.

To measure effectiveness, it was assumed that if a document was relevant then each page from it was

relevant, but only the most highly ranked page

from each document was returned and counted.

We believe this method to be fair, since one way

in which pages might be used would be to rank on

pages but return whole documents, with perhaps some highlighting of the top-ranked pages. Other

strategies for using parts of documents to select whole documents are discussed in Section 3. Also surprising is that the continue strategy,

with restricted numbers of accumulators, is capable of better retrieval performance than the original cosine method, in which all pages are per- mitted accumulators. It would appear that the

mid to low weight terms, while contributing to

retrieval effectiveness, should not be permitted

to collaborate and select documents that contain none of the more highly weighted terms.

184
10.0 -

3

Q.o

1.0 -

0.1

array continue

quit

-1—I I 1 1 1 1

1

—I—I I 1 1 1

1

1000 10000 100000 1000000 Number of accumulators

Figure 5: CPU time of quH and continue

Whilst the results for retrieval effectiveness are encouraging, the results for time are not. Figure 5 shows the cost of these two strategies in terms of

cpu time, with the set A of accumulators stored

as a hash table. The times shown include all processing required to identify the top r = 200

ranked documents, but do not include the cost of actually retrieving those documents, which, in a system such as ours, in which the text is also compressed, involves further decoding effort. All timings are for a lightly loaded Sun SPARCstation Model 512 using a single processor; programs

were written in C and compiled using gcc. While

for values of L less than about 100,000 the con- tinue method takes no longer than the simple co- sine measure when implemented using an array

of accumulators, it is clearly unsatisfactory compared with the quit technique. In the next section we discuss methods by which this situation can be improved.

2.3 Processing long inverted file entries

Compression can reduce index size by a factor of six; for example, for the paged form of TREC

the size reduces from 1,120 Mb to 184 Mb, an

irresistible saving. Compression, however, prohibits random access into inverted file entries, so that the whole of each inverted file entry must

be decoded, even though not every {d, fd^t) pair

is required. This is the reason that the continue

strategy is slow.

The need to decompress an inverted file entry in full can be avoided by including a series of synchronisation points at which decoding can commence [10]. These can be arranged as a series of pointers, or skips, as illustrated in Figure 6.

skip

document numbers

inverted file entry

Figure 6: Adding skips

The skips divide the inverted file entry into a

series of blocks, and to access a number in the

entry, only the skips and the block containing

the number need to be decoded. Appropriately

coded, such skips increase the size of the compressed inverted file by only a few percent, but

can drastically reduce the amount of decoding re- quired. Decode time for a skipped inverted file entry is given hy T = td {2s + Lp/2s) , where td

is the time needed to decode one {d, fd^t) pair, p

is the number of such pairs in the inverted file entry, L is the number of accumulators, and s

is the number of skips. This time is minimised

at s — y/Lp/2. For 10,000 accumulators and

an inverted file entry of length 100,000 (a com- mon figure for queries to the paged TREC), total time including reading from disk on a typical system drops from 0.30 seconds to 0.22 sec- onds [10]. Moreover, under the same assumptions an uncompressed inverted file entry of this length would take 0.30 seconds to read, and so the

skipped compressed inverted file provides faster ranking than even an uncompressed inverted file. To test skipping, inverted files were constructed for several different fixed values of L and

then, for each inverted file, the cpu time for in- verted file processing measured for a range of ac- tual L values. To ensure that inverted files did not become too large, a minimum block size was

imposed, requiring that every skip cover at least four {d,fd,t) pairs. The results of these experiments are shown in Figure 7. As can be seen, substantially faster query processing is the result when the number of accumulators is less than

about 100,000. As predicted by the analysis, the

index constructed assuming that L = 1,000 gives

the best performance when the number of accu- mulators (the variable L in Figure 3) is small.

185
® 10.0 E

1000 10000 100000 1000000 Number of accumulators

Figure 7: Time required by skipping

2.4 Retrieval experiments

The implementation techniques described above,

skipping and limitation of the number of accu- mulators, can be applied to document retrieval as well as to page retrieval. Table 1 summarises

the various resources required and performance

achieved by both document and page retrieval, for various values of L, the nominal number of ac- cumulators. The following points should be noted

in connection with the data in Table 1.

File sizes Stemming was applied during in- verted file construction, but no words were

stopped. All alphanumeric strings were

indexed, totalling 333,000,000 term occurrences of 538,244 distinct terms. The document index contained 136,000,000 {d, fd^t) pointers, the paged index 196,000,000 such

pairs. Each is compressed to occupy less than one byte. The variation in inverted file size is due to the insertion of skips, or, in the

case of the "All" row, their absence.

The text itself was also stored compressed

using a word-based model [9], allowing the

2,055 Mb to be reduced to 605 Mb. A fur- ther 27 Mb of smaller auxiliary files were

also required. In total, the complete retrieval system for the paged TREC occupied about

817 Mb, or 40% of the initial unindexed text.

Retrieval time The SPARCstation 512 used

for these experiments is approximately 2.2 times faster than the SPARCstation 2 used

for the first round of experimentation [6, 9]. The times listed cover all activity from issue

of query until a ranked list of 200 document

numbers is calculated. Fetching of the 200 answers, which occupy

226.9 Kb compressed, adds a uniform 1.9 sec per query including the cost of decompression. Decompressed, there is an average of

829.9 Kb of output text per query. Elapsed times during the ranking process

are generally about 1.6 sec greater than cpu

time, in the course of which an average of

42 disk accesses are made to the lexicon; 42

disk accesses made to the inverted file itself (fetching a total of 1.65 Mb of data, containing about two million compressed (d, fd,t) pairs); and 275 accesses to the combined file of document weights and addresses. All of

these files except the inverted file are small,

and likely to have been buffered into main

memory, hence the small overhead.

Elapsed time for the presentation of documents was about 5.0 sec per query greater

than cpu time, caused by the need to per- form a further 200 seeks into the file containing the compressed text. This file is too

large for there to have been any buffering ef- fect. Memory space When skipping is employed

(the first three rows in each section of the ta- ble) memory space during query processing

is proportional to the number of non-zero ac- cumulators. In these experiments a hash ta- ble was used, and an average of 14 bytes per

accumulator required. For the "All" exper- iments an array of accumulators was used,

2.8 Mb for document retrieval, and 6.6 Mb

for page retrieval. An array of 6-bit approximate document

lengths was used to guide the retrieval process [11], requiring 0.5 Mb for document re- trieval and 1.2 Mb for page retrieval. Terms processed The values for "Terms processed" indicate the average number of terms

processed before processing switched from

the first to the second phase of continue. For each query a whole number of inverted file entries were processed, and this is why the "non-zero accumulators" average is greater

than the target value L. Not surprisingly,

with page retrieval fewer terms were re186
Inverted file Retrieval Time Non-zero (Mb) (cpu sec) accumulators Doc. Page Doc. Page Doc. Page

L = 1,000 145.8 205.4 2.36 2.95 2,330 2,701

L = 10,000 156.7 220.3 4.46 5.57 12,855 13,238

L = 100,000 161.6 230.2 9.80 12.00 107,737 109,370

All (array) 128.6 184.4 7.66 12.48 582,783 1,307,115

(a) Resources required

Terms Precision at 200 Pessimal llpt

processed average Doc. Page Doc. Page Doc. Page

L = 1,000 3.08 2.84 0.271-0.612 0.260-0.639 0.164 0.166 L = 10,000 6.80 6.06 0.346-0.530 0.319-0.554 0.185 0.173 L = 100,000 16.84 14.26 0.333-0.446 0.326-0.504 0.175 0.175

All (array) 42.44 42.44 0.331-0.444 0.321-0.502 0.174 0.173

(b) Retrieval effectiveness

Table 1: Document vs. paragraph retrieval: (a) resources required, and (b) retrieval effectiveness

quired, on average, to consume the available

accumulators.

Precision at 200 The range of values in this column is to show the fraction of unjudged

documents that w^ere accessed. The lov?er number assumes that all unjudged documents are irrelevant; the upper is calculated

assuming that all are relevant.

llpt effectiveness The "llpt average" values

are calculated based solely upon the top 200

ranked documents, assuming that all remaining relevant documents are ranked last, and

that all unjudged documents are not rele- vant. Index construction New algorithms have been

developed for index construction. Using

these algorithms, the index was built in under 4 hours, using a peak of 40 Mb of main

memory and less than 50 Mb of temporary

disk space above and beyond the final size of the inverted file. The compression of the

documents took a further 4 hours, for a total database build time of under 8 hours.

Based upon these experiments we conclude that:

• use of a limited number of accumulators does not appear to impact retrieval effectiveness,

and so is an extremely attractive heuristic for ranking large collections because of the dra- matic savings in retrieval-time memory us- age that result;

• introduction of skips to the compressed in- verted file entries significantly reduces processing time in this restricted-accumulators

environment;

• index compression can, in this way, become

"free": if only partial decoding is required,

the input time saved by compression can be more than enough to pay the cpu cost of frac- tional decoding;

• all non-stopped terms should be allowed to contribute to the ranking, and that the quit

strategy is inferior to continue; and

• even relatively simple pagination gives re- trieval not mecisurably inferior to document

retrieval. When dealing with pages, the retrieval system was implemented to display the entire document,

but highlight the page or pages that had caused

the document to be ranked highly. This deci- sion made the paged collection particularly easy

to use, and when we browsed the answers to the queries it was very satisfying to be able to

find the exact text that had triggered the match.

This advantage of pagination became clear even

in the early stages of the project, while we were attempting to debug the implementation of the

cosine method.

On the other hand, a problem that was

brought out by these experiments was the dif- ficulty of comparing retrieval effectiveness in

187
the face of non-exhaustive relevance judgements.

When precision rates are around 30%, and a fur- ther (in the L = 1,000 case) 30% of documents

are unjudged, there can be no significance whatsoever attached to the difference between even 30% precision and 40% precision. Indeed, as- suming that 27.1% of the unjudged documents

are relevant for the "L = 1,000; Doc"' combination gives a final precision of 0.411; the corre- sponding number for the "All; Doc" pairing is only 0.373. Thus, the precision figures of Table 1 are sufficiently imprecise that no conclusion can be drawn about the appropriate value of L that

should be used, and about the merits of document vs. paged retrieval. There is clearly scope

for research into other methodologies for comparing retrieval mechanisms.

3 Structured documents

Many of the documents in the TREC collection are very large and have explicit structure, and

it may be possible to use this structure—rather

than the statistically based pagination methods

described above—to break documents into parts. In particular, many documents can be broken up

into a set of sections, each section having a type.

There has been relatively little work done on re- trieving or ranking partial documents. However,

Salton et al. [12] have demonstrated that document structure can be valuable. Sometimes this structure is explicitly available [2], and sometimes

it has to be discovered [5], but the knowledge

of this structure has been shown to help deter- mine the relevance of sub-documents. In this part of the work we used a small database to investigate whether retrieval of sections helped

document retrieval, and whether retrieval of documents helped section retrieval. By way of a benchmark, the paged retrieval techniques de- scribed earlier were applied to the same database.

3.1 The database

Since we needed information about the relevance

of sections to queries it was not possible to use the

full TREC database. Instead, we used a database

consisting of 4,000 documents extracted from the

Federal Register collection. These documents

were selected as being the 2,000 largest documents which were relevant to at least one of topics

51-100 provided for the first TREC experiment.

Another 2,000 documents were randomly selected from the Federal Register collection to provide

both smaller documents and non-relevant docu- ments. The average number of words in these

documents was 3,260.

These documents were then split into sections

based on their internal markup. The documents

had a number of tags inserted that defined an in- ternal structure. It appeared that only the T2.

and T3 tags could be reliably used to indicate a new internal fragment. Section breaks were defined to be a blank line, or a line containing only

markup, followed by a T2 or a T3 tag. This led to a database of 32,737 sections. Each of these

sections had a type based on its tag. The types were (purpose), (abstract), (start), (summary),

(title), (supplementary), and a general category

(misc) that included all remaining categories.

Having made the document selections, only 19

of the queries 51-100 had a relevant document

in the collection. Each of the sections for documents that had been judged as relevant was

judged for relevance against these queries so that

finer grained retrieval experiments were possible. One difficulty that arose was that quite a few documents that had been judged relevant ap- peared to have no relevant sections—there were

relevant key terms in the documents but the documents themselves did not appear to address the

information requirement. There were 145 such

(query, document) pairs. To be consistent, we

took these document to be irrelevant. After these

alterations, only 14 queries had a relevant section,

and there were an average of 23 relevant sections per query.

3.2 Structured r2inking

We carried out a set of experiments on ranking documents using the retrieval of sections. We first compared simple ranking of documents

against ranking sections to find relevant documents. Next, a set of formulae were devised that

attempted to use the fact that one document has

several sections that might be more or less highly

ranked. These took into consideration the rank

of the section, the number of ranked sections, and

the number of sections in the document. Experiment 3 describes one of the more successful for- mulas.

Further trials were then performed using the

type of the section. First, a set of experiments

were run that determined which types were better predictors of relevance. These results were

then used to devise a measure that used a weight

for each type. Finally, we tried to combine these

188
results to see if it was helpful to use the rank of

the whole documents along with the rank of its component parts.

Experiment 1: Rank full documents against

the queries using standard cosine measure. Experiment 2: Split documents into sections. Measure similarity of each section against

the queries using standard cosine measure. Order documents based on the highest

ranked section. Experiment 3: Split documents into sections. Measure similarity of each section against

the queries using standard cosine measure. Order documents based on

where s„ is the n"* highest ranked section in the document. The effect of this formula is that a document's weight is determined by

a decay formula using the weight of all of a document's sections. (Values other than 0.5 were tried, but gave poorer performance.)

Experiment 4: Split documents into sections. Measure similarity of each section against

the queries using standard cosine measure. Then weight each section using its type, for example (introduction) or (address). Order

documents based on

^tw{type{sn))0.5''-^wt{sn)

where s„ is the n*'' highest ranked section in the document, type(sn) is the type of the sec- tion, and tw(ti) is the weight of the type ti. The weights of the types of sections were obtained by conducting a set of experiments

where all types but one were given a weight

of 1, and in turn, each type was given a weight of 2. Using these experiments it was

determined that (purpose) and (summary)

were each more helpful, and that (misc) was

less helpful. As a result, in this experiment iiD((purpose)) = (summary)) = 2, f«;((misc)) = 0.5, and other weights were set to 1. Experiment 5: Rank the documents, and rank

the sections. Form a new rank based on the

average rank of these two ranks.

Other experiments were carried out using best

two sections, and formulas that more closely ap- proximated the cosine measure. None of these

experiments achieved better results than the ones

displayed here. The obvious conclusion is that if documents are available for ranking as whole documents, then for this collection it is preferable to do so.

3.3 Section retrieval

For very long documents it may be desirable to return relevant sections rather than relevant documents. We were interested to see whether it might be useful to know about the rank of the

containing document. In the first experiment

documents were ranked, and sections shown in document order. This produced very poor re- sults. Next, we still ranked sections in higher

ranked documents ahead of lower ranked documents, but used section ranking for sections in the same document. This was reasonable but

there were still many irrelevant sections being ex- amined. Finally, we attempted to delete these ir- relevant sections by using document ranking, and

then section ranking, but this time discarding sec- tions that had a section rank of greater than 200. Experiment 6: Rank sections against the

queries using standard cosine measure. Experiment 7: Rank full documents against

the queries using standard cosine measure. Order the sections by their appearance

within documents.

Experiment 8: Rank full documents against

the queries using standard cosine measure. Order sections, first by document, then by

rank within documents.

Experiment 9: As in experiment 3, but then

delete all but the 200 highest ranked sec- tions.

These experiments show that ranking both

documents and sections does help to find more

relevant sections. This result is in contrast to the earlier investigation of finding relevant documents. We are able to find relevant sections much

easier if the rank of both the sections, and the

containing documents are taken into account.

3.4 Paged versus section retrieval

Our results showing that retrieving documents

based on section ranking was not as useful as

189
Documents / Experiment

5 10 15 20 25 30 50 200

1 0.286 0.271 0.248 0.236 0.234 0.229 0.206 0.102

2 0.243 0.221 0.214 0.204 0.191 0.178 0.170 0.092

3 0.271 0.250 0.229 0.221 0.206 0.202 0.184 0.094 4 0.329 0.257 0.233 0.229 0.206 0.202 0.184 0.085

5 0.343 0.264 0.238 0.236 0.234 0.231 0.209 0.099

Table 2: Comparison of ranking formula for fixed number of documents returned

Documents / Experiment5 10 15 20 25 30 50 200

6 0.186 0.164 0.181 0.161 0.160 0.152 0.140 0.120

7 0.100 0.121 0.105 0.100 0.094 0.088 0.090 0.083

8 0.171 0.121 0.114 0.100 0.091 0.083 0.100 0.082

9 0.214 0.171 0.186 0.189 0.189 0.193 0.179 NA

Table 3: Comparison of ranking formula for fixed number of sections returned

document ranking was perhaps surprising—other

studies have indicated that considering smaller

fragments was helpful, although in combination

with larger contexts. We wondered whether the

section boundaries that had been imposed were

inappropriate. We thus applied the pagination

techniques described in Section 2.1 to the 4,000

document database used here. These results are shown in Table 4 as Experiment 10. These three experiments are a little difficult to interpret. Dividing documents into sections

leads to poorer retrieval performance, but dividing further into pages leads to comparable re- trieval performance to ranking whole documents.

It may be that the manually supplied divisions are poorer than the divisions generated by au- tomatic techniques [5]. Experiments 2-4 show

that some of this performance degradation can be ameliorated by taking documents' structure

into account. However, these experiments indi- cate that there is no retrieval advantage in breaking the document up, should the desired unit of

retrieval be whole documents.

4 Conclusions

The combination of a restricted-accumulators

policy and the introduction of skips to the compressed inverted file entries allows fast query

evaluation on large text collections. Moreover,

the ranking can be carried out within modest

amounts of main memory. For example, the

paged TREC collection contains 1.7 million pages, but ranked queries of 50 or more terms can be re- solved within seconds using just a few megabytes

of main memory. These two techniques mean

that large collections can be searched on small

machines without measurable degradation in re- trieval effectiveness. In the second part of the experiment we have

concentrated on large documents, breaking them

into smaller units for the purposes of indexing.

It is not clear that users are interested in re- trieving 3 Mb documents, and these experiments

were designed to allow users the option of retriev- ing smaller parts of such documents. The results were mixed. It appears that indexing both sec- tions and documents is helpful in ranking sec- tions. However, it is not clear what an appropriate indexing strategy is if only full documents are

to be returned. We are continuing our investigation of partial document retrieval.

Acknowledgements

We would like to thank Daniel Lam and Neil

Sharman for their assistance with various components of the experiments described here. This

work was supported by the Australian Research

Council, the Collaborative Information Technology Research Institute, and the Centre for Intel- ligent Decision Systems.

190
GE in TREC-2: Results of a Boolean Approximation Method

for Routing and Retrieval*

Paul S. Jacobs

GE Research and Development Center

Schenectady, NY 12301

psj acobs@crd. ge. com

Abstract

This report describes a few experiments

aimed at producing high accuracy routing and re- trieval with a simple Boolean engine. There are several motivations for this work, including: (1)

using Boolean term combinations as a filter for

advanced data extraction systems, (2) improving

"legacy" Boolean retrieval systems by helping to automate the generation of Boolean queries, and

(3) focusing on query content, rather than re- trieval or ranking, as the key to system perfor- mance. The results show very high accuracy, and significant progress, using a Boolean engine

for routing based on queries thai are manually

generated with the help of corpus data. In addition, the results of a straightforward implementation of a fully automatic ad hoc method

show some promise of being able to do good au- tomatic query construction within the context of

a Boolean system.

1 Introduction

Full-text search is currently the simplest and most

commonly-used method for locating information in large

volumes of free text. Because users are accustomed to describing what they are looking for with specific words,

and those words are often found in the texts, searching the

text for selected words or word combinations is a natural

and easy-to-implement method for information retrieval. However, it can be very inaccurate. It can be especially

difficult for searchers to compose "queries" that combine

the words that are effective in locating relevant material

without finding large quantities of irrelevant information

ais well. One way to cope with this diflRculty, while still preserving the advantages of the full-text search engine,

*Thjs research was sponsored in part by the Advanced Reseaxch

Project Agency. The views and conclusions contedned in this doc- ument are those of the authors and should not be interpreted as representing the official poUcies, either expressed or implied, of the Advanced Research Project Agency or the US Government.

is to help to automate the process of generating Boolean

queries. This was the focus of GE's TREC-2 effort. GE's involvement in TREC represents a relatively low

level of effort aimed at bringing together natural language

text processing, data extraction, and statistical corpus

analysis methods. Our project uses innovative approaches

for extracting information from text, best exemplified in our results in the MUC and TIPSTER extraction evaluations [7, 3] and in operational text management systems

in GE. In TREC-1, we attempted to show the benefit

of natural language interpretation by using Boolean ap- proximation to select portions of text that could be fur- ther interpreted. The main result of this was that natural

language seems to have very little to offer as a precision

filtering method, because routing and retrieval problems

stem largely from having the wrong terms in the queries

[6]. Thus, in TREC-2, we have stuck with the Boolean

engine, concentrating on the use of corpus analysis to improve the queries.

Figure 1 summarizes our TREC results. Our results in TREC-2, as in TREC-1, were quite good relative to other

systems. The manual routing system, which comprised

over 99% of our eff'ort, produced an 11-point average of

.3308, with an average of 45 relevant documents in the top

100. This put GE's system at the very top of the manual routing category (the system with the best 11-point

average in this category was slightly higher on the 11- point average and had slightly fewer relevant documents, on average, in the top 100).

The residual eff'ort went into a fully automatic ad hoc

method, which produced an 11 point average of .2183 and

an average of 37 relevant documents in the top 100. As in TREC-1, performance varied dramatically by topic. The

routing system showed the best results (in terms of precision at 100 documents) on 8 of 50 topics. Yet it was below

median on 17 topics. This not only suggests areas for fur- ther improvement, but also shows an important difference between the Boolean approach and some of the statistical retrieval systems. The Boolean approach does much better on certain topics, but the statistical approaches have more consistent performance.

191
AD HOC TEST ROUTING TEST

1 1 -pt.

avg. Rel.@

100 docs.

11 -pt.

avg. Rel.@

100 docs.

Boolean '92 .2029 47.2 .2078 35.6

Pattern matcher '92 .1961 46.2 .1851 34.6

Avg. median run '92 .1585 39.7 .1246 28.6

Boolean '93 .2183* 37* .3308 45

Avg. median run '93 .2620 41 .2910 41

fully automatic

Figure 1: Summary GE Results on TREC-1 and TREC-2

While it is very hard to measure progress between

TREC-1 and TREC-2 because none of the numbers are

directly comparable, the difference between our .2078

routing average in TREC-1 and .3308 in TREC-2 is large

enough that we are quite pleased with the rate of progress and confident that we are nowhere near the peak that can be achieved with manual, Boolean routing. In addition,

the automatic ad hoc method that we tried, while showing terrible performance on some of the more convoluted

topic descriptions, had a better average than our manual

ad hoc system last year. Thus, there is a great deal to be gained using cor- pus analysis to automate or assist in query generation, even within the context of straightforward retrieval methods. In addition to having promise for "legacy" operational systems, these results suggest that natural lan- guage methods, focused on corpus analysis and query generation, probably can help in improving the performance

of many information retrieval systems.

2 Boolean Approximation

The basic approach in our system is to compile queries

into Boolean tables that can be matched at high speed

against a stream of input text. This approach is meant

for routing, and also to be compatible with "downstream"

analysis such as what we do in TIPSTER data extraction. In fact, the Boolean compiler we use is designed for handling the much more complex expressions that our system

uses in data extraction.

Figure 2 illustrates the approach. We call this Boolean

approximatton because the Boolean expressions used in the basic matching engine are an approximation to more

detailed processing of texts, in the sense that they are guaranteed to admit all text that would be admitted

by more detailed processing, but will usually also admit

many texts that would be rejected by more detailed constraints. This is a very general method, in that the sys- tem can be configured to apply many different stages of

analysis, from "shallower" processing to "deeper" inter- pretation, with each stage applying stricter constraints

for example, word order, proximity, semantic constraints,

and so forth. Furthermore, at each stage, the effects of

filtering can be measured, generally showing a loss of recall and gain in precision. In TREC-1 [6], we measured

this tradeoff and found that the highest 11-point averages came from the first stage of filtering; in other words, the

gains in precision in later stages were not enough to make

up for the loss of recall on these measures. The figure also illustrates the flow of information at development time and the sort of knowledge that applies

at each stage. For example, at development time, knowledge can be mapped from the deeper levels into shallower

levels. At run time, the subsequent stages of language

analysis apply this knowledge in stages.' For example,

our system can analyze joint venture texts (in English

and Japanese), looking for, among other things, infor- mation about the joint venture company by recognizing

that the company is often the object of the verb esiabhsh. In the Boolean stage, this can be approximated by

looking for the combination of words like establish with

words like venture. In the finite state pattern matching

stage, the system might look for any word with the root

establish followed by the venture term (and perhaps the

reverse order with the verb in a passive form). In deeper

interpretation, the system applies syntactic and semantic

constraints to recognize the different ways that the con192
Development time

knowledge base compilation

Run -time control flow

C

Automatic Boolean

query generation

(ad hoc)

Statistical and manual

corpus analysis

Lexico -semantic

regular expressions

Lexicon,

grammar,

knolwedge bases

Boolean approximation

(establishi OR establistied)

AND venture

Finite state pattern

matciiing

(root establisti) * venture

Natural language

semantic interpretation

(c-establistiing

(r- effected (c -venture))

Routing/

Retrieval

Extraction

Interpretation

Figure 2: Approximation and Natural Language Processing

cept of establishing can be expressed, and insure that the

words appear in a grammatically acceptable way in the

input. This more detailed analysis can be crucial in data

extraction tasks. A critical point about this model is that even detailed

knowledge about language often ends up contributing to the Boolean tables, so the Boolean expressions are considerable more complex than those that could easily be

created by hand. Furthermore, the Boolean expressions

are a relaxed form of more complex knowledge-based constructs. It is very difficult to predict the impact of simply relaxing constraints. For example, TREC topic 53

includes the phrase "leveraged buy-out" . In a strict syntactic analysis, this phrase would have to appear exactly

in that form, enforcing both proximity ("leveraged" adjacent to "buy-out") and order ("leveraged" before "buyout"). But relaxing such constraints seldom has any se- vere effect on results: the Boolean expression (leveraged

AND buy-out) in our system will recognize the two terms

occurring anywhere in the same paragraph, which is actually likely to admit more texts about leveraged buy-outs

than admit texts in which the words coincidentally ap- pear together.

Even the phrase "prime rate" , which matches some ir- relevant texts in its Boolean form (including, for example,

one or two texts about Japan that mention "prime minister" in the context of "growth rate"), also admits some

additional relevant texts in which "prime" appears in the same paragraph as "rate" . The well-known effects of word

order and proximity on meaning, exemplified by the dis- tinction between "blind Venetian" and "Venetian blind"

do not seem to appear very frequently in real examples.

At least, these effects may be less frequent

In TREC-2, we did not apply any constraints stricter than Booleans at run-time; in other words, we used only a Boolean retrieval engine (because in TREC-1 we proved

that the stricter constraints didn't help). We also had to implement a module to relax some of the "hard" Boolean

constraints for topics with a very small number of relevant documents, because of the nature of the performance metrics. However, we still used the knowledge that was devel- oped for more detailed processing, including the phreises,

semantic groupings and so forth. In addition, we added

a more sophisticated ranking mechanism than we used in TREC-1, because ranking is very important in the evaluation. But the only retrieval engine in our TREC-2 system

is a Boolean matcher.

2.1 The Boolean Matcher

The Boolean tables are efficiently organized so that a C

program (which we now know as NLgrep) can match

them against incoming texts at a rate of about 1 million

words per minute. This program spends little time on

anything other than marking where words in each document match terms in the table. In both routing and

retrieval, the total number of terms used for a set of 50

topics is about 2000, or about 40 unique terms per topic. All other words are ignored entirely. For example, the following is a query for the topic

"South African sanctions" (using the enhanced regular

expression language of GE's pattern matcher [5]):

193
Seuiction == [(member sanction sanctions

disinvestment)

<Sullivaii Principles>

<pimitive *2 measures>] ;

safrica == [(member Buthelezi Pretoria

anti-apcirtheid apartheid)

<De Klerk>

<South (member Africa Africcin)> ] ;

; ;

; rule 1 $sanction * $safrica => (mark-topic 52) ;

This description says that any matching text must have

both an indicator of South Africa ($safrica) and one of

sanctions ($saiiction), and that the sanction phrase and

South Africa phrase must appear in the same paragraph

in the document.

A sanction phrase can be any of the simple words sanciion, sanctions, or disinvestment, or any phrase including punitive measures with no more than two intervening

words (like punitive economic measures). A South Africa

phrase can also be either one of a group of simple words,

or a phrase, like De Klerk, South Africa, or South African.

These queries or topic descriptions can be quite complex, and the method has been designed to handle many

queries simultaneously, so the rule compiler is designed to produce expressions that can be efficiently applied within a large set of queries. This is important because many

queries can share the same simple terms or combinations

of terms, and because the Boolean matcher must match

the simplest expressions first. For the topic description given above, the output of the

rule compiler will include the following tests:

52 TERM AFRICAN

2029 TERM MEASURES

2134 TERM SANCTION

2135 TERM SANCTIONS

2136 TERM DISINVESTMENT

2138 TERM SULLIVAN

2139 TERM PRINCIPLES

2141 TERM PUNITIVE

2144 TERM BUTHELEZI

2145 TERM PRETORIA

2146 TERM ANTI-APARTHEID

2147 TERM APARTHEID

2149 TERM DE

2150 TERM KLERK

2152 TERM SOUTH

2153 TERM AFRICA

2137 OR 2134 2135 2136

2140 AND 2138 2139

2142 AND 2141 2029

2143 OR 2137 2140 2142

2148 OR 2144 2145 2146

2151 AND 2149 2150

2154 OR 2153 52

2155 AND 2152 2154

2156 OR 2148 2151 2155

2157 AND 2143 2156

2158 AND 2156 2143

T0PIC052 OR 2157 2158

Each line in the above data gives a unique number

(or topic designator) to the test, a test identifier (either

TERM for a simple word test, OR, or AND), and a list of simple terms or previous tests. For example, test 2137

depends on tests 2134, 2135, and 2136, and is true if any

of those tests is true, namely, if the text includes any

of the words sanction, sanctions, or disinvestment. The

tests are automatically ordered so that all tests that are dependent on other tests will have higher numbers than

the tests they depend on; thus all TERM tests appear

first. In this case, the TERM test AFRICAN appears

with a much lower number simply because it is used in many diff'erent queries.

The matcher, which can work either on complete documents or paragraphs (but we used paragraph matching

only in TREC-2) goes through every word in its input

and, using a fast table look-up, sets the TERM tests to

true for every word it encounters. At the end of input, ei- ther the end of the paragraph or end of each document, it runs through the table of possible tests from low numbers

to high numbers and sets tests to true if their conditions

are satisfied. A topic test produces a match if it has be- come true at the end of this process, meaning that the

paragraph or document has passed the pre-filter for that

query. A single paragraph, of course, can satisfy multiple

queries.

This portion of the system was implemented in the

space of a few days, and is almost entirely the same as in TREC-1. Our focus since last year has been on query construction and ranking rather than matching or retrieval.

2.2 Query construction

Our approach assumes, in general, that manual query

construction is acceptable for routing. In ad hoc retrieval,

query time can be of the essence, but in many routing

applications, queries are developed and refined over time.

The amount of time spent on query construction using a manual method in our system is comparable to the

amount of time spent on the topic descriptions used for automatic query generation.

194
2.2.1 "Manual" queries for routing

In manual routing, our approach uses a statistical cor- pus analysis, developed originally for text categorization

[4], to pull out terms based on their relative frequency

in relevant documents for each topic. The statistic used

combines the entropy-based mutual information statistic (testing the independence of each term with each topic)

with a correction for low-frequency terms and for ambiguous words. Words with high weights have a high degree

of association with a topic. This statistical analysis is also used in ranking. The base weighting formula is the

following:

C(log2 6)(log2r)

where C is a constant, b is number of times a term

appears in a story assigned to a particular category, for example, and log2 r is the log of the ratio of combined

probabilities (i.e., of a particular word or phrase occurring in a text about a particular category) to the product of independent probabilities—the mutual information

statistic. This tests the assumption that the use of the

word and the category of the text are independent. When

this assumption is false, the word gets a high positive or negative weight. For example, the following are the top words for Topic

51, "Airbus subsidies":

A-330 T0PIC51 1263.3

AIRBUS T0PIC51 1183.1

A-340 T0PIC51 1178.2

INDUSTRIE T0PIC51 1071.9

A-320 T0PIC51 1067.5

MESSERSCHMITT-BOELKOW-BLOHM T0PIC51 i AERONAUTICAS T0PIC51 843.3

CONSTRUCCIONES T0PIC51 807.9

AEROSPATIALE T0PIC51 762.5

MBB T0PIC51 722.6

WIDE-BODY T0PIC51 617.7

MD-11 T0PIC51 613.8

TOULOUSE T0PIC51 228.5

JETLINERS T0PIC51 217.8

LUFTHANSA T0PIC51 196.6

MD-80 T0PIC51 196.6

Clearly, these words all have some reason to be associ- ated with this topic, but adding them to the appropriate

group in each query (or ignoring them entirely) is a "manual" process. Our manual routing queries, therefore, are a combination of the regular expressions that were devel- oped from the topic descriptions with terms added that were selected from the automatic training. This is, we

believe, a very practical manual approach that has very good performance.

2.2.2 "Hard" vs. "Soft" Booleans

The Boolean matcher uses a "hard" Boolean approach, in that it will admit only texts, for each query, that satisfy

the conditions of that query. For example, in Topic 51

above, "Airbus subsidies", the matcher will allow texts only that have both and Airbus term and a subsidy term

in the same paragraph. However, this is a narrow topic,

and TREC-2 allows each system to produce 1000 texts

for each topic. The evaluation metrics offer no penalty

for filling up the list of 1000 with texts that are likely to be irrelevant. So, in order to provide increeised flexibility

and consider larger numbers of texts for each topic, we

used an additional engine only for the purpose of pulling

in texts for very specific queries like this one. The system is still a hard Boolean system in that texts that satisfy the Boolean conditions will always be ranked

higher than texts that do not satisfy the conditions; however, texts that do not satisfy the conditions can appear on the final ranked lists. The "soft" engine considers such

texts by relaxing some of the Boolean conditions, effec- tively pulling in texts that have a large number of terms

that match the query, but do not necessarily meet all the

conditions. This component of the system is more like statistical retrieval engines; however, it does not have a

large impact on the overall scores, because it only af- fects the results at the low-precision extreme (the lowest rankings) for queries that match very few documents.

In fact, for Topic 51, the hard Boolean query matches

11 texts, and the soft method pulls in an additional 989

texts. But there are only 11 texts that are judged rele- vant, of which 10 satisfy the hard Boolean. So enforcing

the hard Boolean condition seems to work well for this topic, and the soft Boolean doesn't contribute much.

For some topics, the balance between the hard Boolean

condition and the soft Boolean isn't so clear; i.e. not en- forcing the hard condition would lead to better ranking.

This seems be a function of how well the topics fit with

Boolean expressions in general. "Airbus subsidies" is re- ally a Boolean topic, in that a relevant text must say something about Airbus and something about subsidies. Other topics like "automation" are much harder to ex- press in a Boolean form. We will cover these issues in topic-by-topic performance later in this paper.

2.2.3 "Automatic" queries for ad hoc retrieval

The "manual" query method is partly automated in the

sense that the corpus-based statistical training suggests

many of the terms that are used in the queries. But it is "manual" in that the initial formulation of the queries

is done manually from the TREC topic descriptions.

We have tried, as a simple experiment, to generate the

Boolean term groupings and expand each term automatically from the topic descriptions. In the days before the

TREC-2 ad hoc test, we tried several different ways of do195
ing this automatic Boolean query generation, and chose

the one that worked best on the sample data.

Our first attempt was to use the common methods for finding collocations and word associations in sentences,

and these worked horribly for term expansion. The problem is that this approach finds more associations like "fu- neral" and "home" than it does "hostage" and "captive"

,

and the latter, text-level associations are what's required

to generate good queries.

The "solution" we tried was, for a sample of about 10

million words in the corpus, to choose the top 20 words

based on TF.IDF weights for each document, store the

frequency of association among these terms, and then

weight each pair using the weighted mutual information

statistic of the previous section. This was much better

than using sentence-level information, although it is still a very straightforward approach. For example, the fol- lowing are the top 10 terms associated with the word

"hostage" (in order):

hostages

Lebanon

Beirut

Ircin

release

Terry

kidnappers

kidnapped

Jihad

Anderson

While this is certainly not the optimal set of terms to use in place of "hostage", it is a good start. The next problem in automatic query construction is when to use a combination of terms and when to use a single term. For example, the term "weather-related

fatalities" is a combination of two word groups (weather

and fatalities) while "Iran-contra affair" is really only one group (Iran-contra), even though it might appear that

"aflfair" is a significant term. Again we took the direct approach, choosing to combine terms whenever there was a reasonable percentage of

overlap between their associated terms. This worked sur- prisingly well in cases where the topic title was a good de- scription (e.g. "welfare reform") and very badly for those

with vague titles (e.g. "find innovative companies"). We

tried to recover from these by including more words from

the description and narrative, but then we had to start recognizing the language of these descriptions, filtering out words like "relevant", "mention" and so forth. At this crude stage, the main problem with the query generation

method is using the structure of the topic descriptions.

The second major issue with automatic query generation is that it isn't nearly as good at finding good terms

as the process of training from data and relevance judgements, as used in the routing experiments. The relevance

judgements used for routing contain large volumes of rela- tively high-accuracy data, while the training used for term

expansion in query generation relied on relatively small

volumes of relatively noisy data. For example, the word

"welfare" used in one of the ad hoc topics occurred with a high enough TF.IDF weight only 29 times in the training

sample, and the most frequently associated term, "chil- dren" , occurred only 6 times. In order to establish good

associations between "welfare" and less frequent terms, we would need much more data. The data from TREC-2

seem to suggest that low-frequency terms contribute more

in term expansion than high-frequency terms, so using a

"small" training sample (10 million words is only about

3% of the corpus) was a major error. We made many

other mistakes in the training method, including mixing

samples from the Federal Register and DOE sources with

other texts that are much more likely to be relevant. This

leaves a lot of room for future experiments and improvement.

The fully automatic ad hoc system certainly didn't do

as well as the manual routing system, but it was still at or above median for more than half of the ad hoc topics.

Considering that this method could be used within the context of most any legacy retrieval system, the result

is worth noting. Furthermore, the generation of Boolean

queries from natural language descriptions is an interesting, as well as practical, research problem, because many

diff"erent retrieval systems can make some use of Boolean

queries.

3 Ranking

In both routing and ad hoc, we used a set of word weights

for ranking, acquired using the relevance judgements in the routing case and from the corpus data in the ad hoc

case. In routing, the weights reflect the statistical measure of association between the term and each topic (using

the weighted mutual information score given earlier). In the ad hoc case, the weight is a function of the frequency

of the term in the topic description, the inverse collec- tion frequency, and an additional factor to weight certain

components of the topic descriptions (such as the title and

description) more heavily than others. We combined the

weighted frequency of these terms with an overall count

of the number of topic hits per document, normalizing for document length, to produce a score for each document.

This was the result of trying many diff"erent approaches on the test data, so it was definitely a good method for our system.

However, in comparing our results with those of other

systems, our precision curve across various recall points

is not nearly as good as a system that does really good

ranking. In routing, we are not sure that ranking is important, but it is certainly important in getting good re- sults in TREC. So, we are inclined to try to combine our

196
retrieval method with alternative ranking methods to see, for example, whether more terms are really necessary in order to get better ranking results. The separation of retrieval and ranking seems to be a valuable tool both for experimental research and for identifying different techniques for applications. It is clearly a problem with both TREC-1 and TREC-2 that the routing

task requires a comparison of documents across a large

collection, when most routing applications deal with a stream of documents individually or in small groups.

4 Analysis of Results

The results raise a number of important issues, especially: why Boolean approximation works as well as it does, particularly why it works for routing; where statistical weighting could help more; what sort of topics this approach does well on (and which topics it does badly

on); and other obvious areas for improvement.

One of the most important sources of information

about the advantages and disadvantages of each approach comes from comparing the performance of different sys- tems on different topics. Unfortunately, this is also a

very difficult task, because, while it is easy to tell which

systems did well on which topic, it is often hard to generalize from that evidence why the approach worked or why

it didn't. As we have mentioned, the Boolean approach is very

erratic with respect to performance by topic, as compared

with other systems, particularly the statistical methods

that emphasize weighting. For example, our manual rout- ing system, which was clearly one of the best systems, had

the top performance (in precision at 100 documents) on 8

topics, but was below median on 17 topics (out of 50). In the 11-point averages, that system was below median on

22 topics—more than 40% of the time—although it out- performed most of the systems on average. By contrast, one of the Cornell systems [1] was above median on ev- ery topic! This suggests that our approach degrades less gracefully than other approaches, and that it is important

to explore Boolean methods as an adjunct to other methods that work in the cases where the Boolean approach

seems to fail. Conversely, our system had top or near-top

scores on a significant number of topics; it is important

to know how to take advantage of this within the context

of weighting systems.

There seem to be several different explanations for vari- ation on the topics. First, there are topics, as we have dis- cussed, that are particularly well suited to Boolean methods (and others that are not well suited at all). Second,

there are cases where the training method seems to work

particularly well. Third, there are cases where the manual approach might work well because there are terms in the topic description that are particularly misleading. Finally, there are many reasons why our approach can fail.

particularly on topics with very small numbers of relevant documents and in cases where the topics are very vaguely

specified.

One of the topics where GE had the best results was

Topic 53, "leveraged buy-outs". The topic description

specified that relevant documents had to describe an LBO

above $100 million in value, and give the terms of the

buy-out. Apparently, the $100 million figure is not important, because most of the LBO's that are reported are major buy-outs. However, the terms (the specification

of the dollar amount) are required. Many articles about

LBO's do not report dollar amounts. This is similar to the "Airbus subsidies" topic, where many articles that

talk about Airbus do not mention subsidies, and they are not relevant. The advantage here seems to be that the

hard Boolean outperforms the weighting approaches be- cause weighting, without Booleans, is likely to give an

article with many LBO words, but no dollar figures, a high weight, just as it could a high weight to an article about Airbus that doesn't mention subsidies. The effect of training seems to help in the "leveraged

buy-out" case as well. The training picked up many

names of companies involved in buy-outs, like "Safeway"

and "Dart" , and these were included in the queries. This

perhaps helped to separate articles about specific buyouts from buy-outs in general. A similar effect came

about on Topic 92, "international military equipment

sales", where the training pulled in names of many of

the weapons typically sold on the international market.

Topic 86, "bank failures" was another topic where the

GE system outperformed all others on both the 11-point

average and precision at 100 documents. This result is hard to explain, but the one conspicuous fact about the

topic is that our query does not include the word "bank"

.

It does include the names of many prominent banks, so it may be, like the LBO case, that good performance on this topic depends mainly on distinguishing specific references

to failures from general discussions about bank failures, for example, the S&L crisis. On the topics that have very few relevant documents,

our approach often failed because, in the absence of train- ing data, it tended to undergenerate; thus very few texts (sometimes none) would match the Boolean query and

other systems with good weighting would pull in more

relevant documents. In these cases, a system that finds one relevant document scores much better than a system

that finds zero, so the penalty for undergeneration is very

high.

The second class of topics where we seem to go wrong

is in those that are vaguely specified. For example. Topic

74, "policy conflict" is a very hard topic, where the description does not include very much information. Texts

rarely mention policy conflict, and, when they do, they

are rarely relevant. On the other hand, texts about to- bacco policies and health are likely to be relevant. This

197
is probably a case where there is no point in having a Boolean query, and probably systems with the best train- ing and weighting methods do best. Another important point about the TREC-2 results is that the best automatic systems did significantly better than the best manual systems in the routing task, probably because of the success of the training methods used. This raises an important question with respect to the rel- ative contributions of time spent on query construction

versus time spent assembling training data. The volume

of training data used in TREC-2, with hundreds of thousands of relevance judgements, is not realistic for most

routing scenarios. Thus, while we should continue to rely on good training methods, we should be careful to sep- arate out the effects of training and to develop manual

routing methods that work with smaller amounts of train- ing data.

Given that Boolean and manual methods seem to do

best on certain topics, and other approaches that emphasize weighting do better on others, it makes sense to combine the best from different approaches. However,

this raises the issue of how different results can be incor- porated into our model without losing the advantages of

the Boolean method, particularly the compatibility with

so many existing systems.

5 Future Goals

Many of our results from TREC-2 suggest areas where the

method of generating Boolean queries, especially using

corpus data, can be substantially improved. There is a

great deal of room for future progress, so we believe that

this approach will continue to be viable with respect to other routing and retrieval methods.

The main area for research is in continuing to ex- plore new corpus analysis methods. Our corpus analyzer

weights single terms. But the Boolean queries depend on

combinations of terms, not only in the case of phrases but

also to control the effect of ambiguous words. In the context of a given query, a single term can often be roughly

comparable to the Boolean AND of two or more other

terms. Up to this point, we have not quite been able to automate the process of discovering these relationships in the corpus. This is important for both routing and ad

hoc retrieval, but especially for routing.

Both the routing and ad hoc systems can benefit from

the use of new ranking methods, and possibly from ex- ploring hybrid approaches that take advantage of the

Boolean method on topics that are well suited to Boolean

expression and degrade more gracefully to traditional

weighting methods on other topics. In general, the combination of methods is something that merits new exper- iments.

The routing system could benefit from new training

methods. Because the Cornell system did especially well

using a training method that produced large numbers of

terms and used both positive and negative information,

it is possible that this general approach could help our system as well. The ad hoc system suffers mostly from difficulties in handling the topic descriptions; our method of deriving

Boolean expressions from the topic descriptions is still ex- tremely crude, and there are many topics for which the

approach produced almost useless queries. The perfor- mance of the automatic ad hoc system was even more

erratic than that of the manual routing system, but it is very likely that many of the problems can be solved with

a lot more work on processing the topic descriptions. Although we are loath to direct research at issues that are particular to the formulation of the TREC topics, this work may be necessary to determine the real power of

automatically generating Boolean queries.

Finally, we are interested in exploring many ways that our corpus analysis and query generation component can be combined with other systems. Because we have fo- cused our attention on query content rather than ranking

or retrieval models, we believe that our results could quite

likely be used within many other retrieval systems. It is natural to look for such synergy. We have had some pre- liminary collaboration with the UMass team to try to use our queries within the INQUERY system [2], but we still have a long way to go. We will continue to explore such

collaborative efforts and to concentrate our own efforts on corpus analysis and building queries.

6 Summary

GE's participation in TREC involved the implementation of a number of strategies for creating Boolean queries

from the topic descriptions. A statistical corpus analyzer

helped to refine queries for both the routing task, and

to generate them automatically for the ad hoc task. The

simple Boolean retrieval engine performed well, especially

in routing. As before, there is tremendous variation in the topic-by-topic results, suggesting that a great deal more research is needed to find how to get the best re- sults in different routing and retrieval scenarios. We are encouraged by the progress of our system, as well as of

the overall field, in these experiments, and are hopeful

that in the coming years we will learn how to combine

our promising results in Boolean approximation and cor- pus analysis with the more mature ranking and retrieval models of some of the other systems.

198
References

[1] C. Buckley, J. Allan, and G. Salton. Automatic rout- ing and ad-hoc retrieval using SMART: TREC-2. In Donna Harman, editor. Proceedings of the Second Text

Retrieval Conference (TREC-2), Gaithersburg, MD,

1993.

[2] W. Bruce Croft. The University of Massachusetts

TIPSTER project. In Donna Harman, editor, Proceedings of the Second Text Retrieval Conference

(TREC-2), Gaithersburg, MD, 1993.

[3] P. Jacobs, G. Krupka, L. Rau, M. Mauldin, T. Mitamura, T. Kitani, I. Sider, and L. Childs. The TIPSTER/SHOGUN project. In Proceedings of the TIPSTER Phase I Final Meeting, September 1993.

[4] Paul S. Jacobs. Using statistical methods to improve

knowledge-based news categorization. IEEE Expert,

8(2):13-24, April 1993.

[5] Paul S. Jacobs, George R. Krupka, and Lisa F. Rau. Lexico-semantic pattern matching as a companion to parsing in text understanding. In Fourth

DARPA Speech and Natural Language Workshop,

pages 337-342, San Mateo, CA, February 1991. Morgan-Kaufmann.

[6] George R. Krupka Paul S. Jacobs and Lisa F. Rau.

A Boolean approximation method for query construction and topic assignment in TREC. In Second Annual

Symposium on Document Analysis and Information

Retrieval, May 1993.

[7] Lisa F. Rau, George R. Krupka, and Paul S. Jacobs. GE NLToolset: MUC-4 test results and analysis. In Proceedings of the Fourth Message Understanding Conference (MUC-4), San Mateo, CA, June

1992. Morgan Kaufmann Publishers.

199

TREC-II Routing Experiments with the

TRW/Paracel Fast Data Finder

Matt Mettler

TRW Systems Development Division

Redondo Beach, CA

Fritz Nordby

Paracel Inc. Pasadena, CA

1.0 Introduction

For TREC-II, we were interested in experimenting with improved methods of constructing

queries for the Fast Data Finder (FDF) text search coprocessor. We learned from TREC-I

that while the pattern matching ability of the FDF can sometimes be put to significant

advantage (we had the high score on 8 of the 50 routing topics in TREC-I), this wasn't

sufficient overall to overcome the weaknesses traditionally associated with the boolean

approach to text retrieval. Many of the TREC topics are too abstract and ambiguous to respond well to a boolean query formulation.

Our goal for this year therefore, was to apply the FDF hardware to a more statistical or soft

boolean retrieval approach while not giving up on our ability to make use of specific

features or patterns in the text when they are obviously important.

We experimented with two different schemes. In the first scheme, we utilized subquery

proximity to rank hit documents. We developed the subqueries manually, then determined

the optimum proximity values by test runs on the training data. The most effective values were then used in the official routing queries. The second scheme was an FDF adaptation

of the traditional Information Retrieval (IR) term weighting approach. In addition to single

word terms, we also included two and three word phrases, and FDF subqueries designed to detect special features in the text.

While in the terminology of TREC both are examples of manual query formulation with

feedback, we believe these techniques can be evolved to create queries automatically from

samples of relevant text and to also incorporate user knowledge of specific text features of

interest when it exists. We also continue to believe that the utilization of a hardware

accelerator such as the Fast Data Finder, enables the implementation of high performance

routing or dissemination applications at a far lower cost than can be achieved with

conventional general purpose processors.

201
2.0 The FDF Text Retrieval Approach

The Fast Data Finder is a hardware device that performs high-speed pattern matching on a stream of 8-bit data. It consists of an array of identical programmable text processing cells

connected in series to form a pipeline processor. The cells are implemented using a custom

VLSI chip designed and patented by TRW. In the latest implementation, each chip contains

24 processor cells and a typical system will have 3,600 cells. Each cell can match a single

character of query or perform all or part of a logical operation. The processors are interconnected with an 8-bit data path and approximately 20-bit control path. To perform

a search, a microcode program is first downloaded into the pipeline to direct each

processor. The database is then streamed through the pipeline. The data bytes clock

through each processor in turn until the whole database has passed through all processors.

As the data is clocking through, the processors alter the state of the control lines depending on their program and the data stream values.

When the pipeline's processor cells detect that a series of database characters match the

desired pattern, a hit is indicated and passed by external circuitry back to the memory of the

host processor and to the user. The FDF pipeline runs at a constant speed as it performs

character comparisons and logical operations, regardless of query complexity.

The queries or patterns are specified in the FDF's Pattem Specification Language (PSL).

The hardware directly supports all the features in the PSL query language without the need

for software post-processing. The processors in the pipeline may all be used to evaluate a

single large query or may be assigned to evaluate numerous smaller queries. The number

of pipeline cells a query needs is proportional to the size of the query, PSL provides

numerous search functions, which may be nested in any combination, including:

• Boolean logic including negative conditions

• Proximity on any arbitrary pattem

• Wildcards and "don't cares" anywhere in the word

• Character alternation

• Term counting, thresholds, and sets

• Error tolerance (fuzzy matching)

• Term weighting

• Numeric ranges

The Fast Data Finder was originally designed and developed at TRW. In 1992, TRW

licensed the FDF technology to Paracel Inc., which now sells a commercial product called

the FDF-3.

3.0 Proximity Query Generation

Our first set of experiments revolved around the use of subquery proximity to rank hit

documents. We began with a simple observation: topics are often a conjunction of ideas or

concepts. For example. Topic 51, Airbus Subsidies, is a conjunction of the idea "Airbus"

(a particular aircraft manufacturer and European consortium) and the idea "subsidy" (in

particular, subsidies from the nations belonging to that consortium). Other articles about

Airbus Industrie (new planes, fly-by-wire in the A320, accident reports, financial health

202
reports, etc.) are not relevant to this topic; nor are articles which describe subsidies not

directed toward Airbus.

Traditional IR term weighting techniques do not give any explicit benefit to articles which

conjoin ideas. Articles which include terms relevant to each of the component sub-topics

will receive high scores; but so will articles which include many terms relevant to only one

sub-topic. Recent efforts implicitly include conjunctions through the use of phrases as terms in otherwise traditional statistical methods.

An altemative is the use of boolean operators. This has the desired effect — an AND of

terms forces a conjunction ~ but the use of booleans in IR has been viewed with some

skepticism and disfavor. Boolean operators often find a conjunction of terms where none

truly exists (for example, Airbus and subsidies might be mentioned in two separate and

unrelated portions of an article); or, if made sufficiently restrictive to eliminate spurious

matches, boolean-based searches often miss relevant articles.

We have followed an approach which incorporates both ideas. Rather than focus on

specific phrases, we search for terms in proximity to one another. The terms in the query

are chosen to represent each of the constituent sub-topics, just as in a boolean search. The

specificity of the query is adjusted by varying the required proximity of the terms. Thus,

for Airbus subsidies we might search for terms representing "Airbus" in a range of

proximities to terms representing "subsidies".

This approach allows conjunctions to be graded. A small proximity restriction (say, 3

words) yields results similar to a keyphrase search, indicating that the two concepts are indeed associated in the article and that the article is relevant to the topic. A large proximity

restriction (1 article) is analogous to a simple boolean keyword search and retreives articles

in which the concept terms may be only loosely associated. Intermediate proximities (1

sentence, 1 paragraph, etc.) indicate intermediate degrees of association and intermediate

recall/precision trade-offs.

It is also possible to use multiple proximities in a single query with this method, or to use

proximities and occurrence frequencies together, to form multi-dimensional arrays of query

parameters. For example, for Topic 62, Military Coups D'etat, the number of conjunctions

was traded off against the proximity of the conjunction to form a two-dimensional query

set.

For the initial experiment, lists of synonyms representative of each idea in a topic were

manually built, and one- or two-dimensional query sets were built from these lists. These

queries were then run against the training database, and after some feedback, the query sets were finalized. Each finalized query set was run against the training database to determine

a ranking of the queries based solely on selectivity.

Table I shows a sample proximity query and Table III shows our TREC-II results. The

number of relevant documents retrieved by the proximity method queries are labeled

TRWl.

203
4.0 Statistical Query Generation

Our second set of experiments revolved around the use of term weighting. Our basic

approach was to follow the well researched path of generating term weights proportional to the occurance of words in a sample of relevant text and inversely proportional to the occurance of words in the database as a whole. Since we were doing the routing topics,

we gathered statistics on Volume II and hoped that they would be valid for Volume III. For

sample relevant documents we used the NIST TREC-I relevant documents from Volume

II. We did not use the topic narratives or descriptions. In addition to single word tenns,

we also considered two and three word phrases. We used the FDF itself to scan the training

corpus and determine the phrase frequencies of interest.

To adapt this standard approach to the FDF, we needed to make three algorithmic

modifications. First, we needed to adapt to the limitations imposed by the FDF hardware.

While extremely effective for pattern matching, the FDF is not a general purpose computer.

While the FDF processor cells can perform basic addition/subtraction, the datapath

available to accumulate an aggregate score for a document is limited to 8 or 9 bits. Thus

we had to restrict the term weights (and the range of their sums) to integer values between

0 and 255 or 0 and 511. This had the effect of truncating most topic's query terms at 10-

20 terms (words, phrases, or special features). We also excluded terms from our queries

that did not appear in at least 30% of the relevant sample documents.

Second, we were striving to not give up the strengths of the FDF's pattern matching

capabilities to pinpoint special features in the text which have a large impact on document

relevance. We manually reviewed the topics and prepared special feature subqueries in an

attempt to increase the precision for particular topics. For Topic 59, Weather Related

Fatalities, we manually prepared a special feature subquery to detect phrases detailing a numeric value of people killed. We determined the frequency of each special feature, both

in the sample relevant documents and in the training database as a whole, and just added

these into the word list as if they were regular single word terms. In some instances our manually prepared subqueries jumped to the top of the list of statistically relevant terms for a topic; in others they didn't.

Third, we observed that some topics had particular words, phrases, or special features that were present in almost all relevant documents. We converted terms that occurred in > 90%

of the relevant documents to boolean ANDs in our queries. This was intended to improve

precision for topics like 62, Military Coups D'etats. The topic narrative specifically stated

that the country involved must be named. One of our special feature subqueries was a list of known foreign country names. While of no statistical significance as a term, this

subquery did hit on almost every sample document. We thus ANDed it into the query as a required boolean term.

Table II shows a sample statistical query. DocCount is the number of documents in the

sample that included the term, phrase, or special feature. DbCount is the number of

documents in our training sample that included the term. Weight is DocCount divided by

DbCount. PslWeight is the integer coefficient based on the Weight. The relevant

documents retrieved by the statistically generated queries are labeled TRW2 in Table III.

204
TABLE I - Sample Proximity Query

Query Template for Topic 75, Automation

/* Concept 1: automation */

define x_auto ' automat [e I ion | ed | es I ing] ' end

/* Concept 2: change in economics */

define x_up_dn ' [increas I decreas] [e | es | ed I ing]

' rais [e I es I ed I ing]

' ris [e I es I ing]

' drop [ I s I ped]

' dip [ I s I ping] '

' expan [d I ds I ded I sion [Is]]

' lower [ I s I ed I ing]

' fall [s I ing]

'hike[ Isid]

' cut [ I s I ting] '

' reduc [e I es I ed| tion [ I s] ]

' end

define x_econ 'cost[|s]' I 'payroll*'

|{ 3 words -> 'work' and 'force') } end

/*

* Query template used for Topic 7 5 */

{ (proxl) -> @x_auto and

{ (prox2) -> @x up dn and @x econ } }

TABLE II - Sample Statistical Query

Term weighting table for Topic 93, NRA Political Backing

Doc sample size is 150

Term DocCount DbCount Weight PslWeight

nra 87 0 130 0 0 66923077 62

national rifle a 145 0 255 0 0 56862745 53

assault weapons 51 0 161 0 0 31677019 30

semiautomatic 68 0 332 0 0 20481928 19

gun_cont rol 73 0 387 0 0 18863049 18

handguns 51 0 319 0 0 15987461 15

rifle 146 0 1336 0 0 10928144 10

handgun 52 0 550 0 0 09454545 9 firearms 63 0 811 0 0 07768187 7 rifles 46 0 1318 0 0 03490137 3 gun 126 0 3866 0 0 03259183 3 guns 96 0 3020 0 0 03178808 3

law enforcement 51 0 2509 0 0 02032682 2 assault 67 0 3491 0 0 01919221 2 ban 91 0 5510 0 0 01651543 2 enforcement 54 0 4825 0 0 01119171 1 weapons 99 0 8993 0 0 01100856 1 association 148 0 16780 0 0 00882002 1 legislation 64 0 7984 0 0 00801603 1 laws 45 0 7662 0 0 00587314 1

205
TABLE III - TRW/Paracel TREC-II Routing Results

NIST Relevant Retr @100 TRWl TRW2

QrvJ: Rel

.

Best Median Worst Rel Rel Description

51 11 11 10 0 10 10 Airbus Subsidies

52 454 100 97 31 99 99 S Africa Sanctiorm

53 154 84 50 1 52 55 Leveraged. Buyouts

54 124 65 57 0 21 47 Sp^I" T.^^nnr*}*! Pont" ol" 55 320 100 87 0 54 96 -L X i O ^ JL X ^ CI v^-L 1 56 395 96 88 3 89 96 i. X. -±- X I X XU. ^ X X Vu/ V %^ 1^ 57 319 91 73 0 61 91 MCI

58 76 62 39 4 49 28 Rail Strikes

59 574 97 80 1 95 95 Weather Fatalities

60 18 9 4 0 2 3 Merit—Pav v? Spniorit

v

61 67 60 24 2 7 24 Israel & Iran—Contra

62 426 92 75 9 82 81 Military Coups D'etat

63 74 40 24 0 24 29 Machine Translation

64 282 68 53 1 29 57 Host age—Taking

65 214 55 29 0 2 46 Info Retrieval Systems

66 86 40 19 0 20 23 NLP

67 365 64 52 0 10 61 Civil Disturbances

68 76 61 38 0 58 55 Health Hazards

69 1 1 0 0 0 0 Revi VI nn ^ATiT TT Treatv

70 34 32 28 0 31 32 Surrocratp Mothprhood

71 300 73 35 1 21 32 Border Incursions

72 91 43 31 0 17 30 Demographic Shifts/U.S.

73 355 74 49 0 23 74 Demographic Shifts/World

74 323 60 32 5 6 35 Conflicting Policies

75 372 59 28 1 59 31 Aut omat ion

76 163 34 24 0 11 34 Original Intent

77 85 49 36 0 36 40 Poaching

78 83 66 58 0 40 57 Greenpeace

79 341 85 75 0 81 77 FRG Party Positions

80 143 54 8 0 54 8 Candidate Platforms

81 4 4 2 0 1 2 PTL Fallout

82 203 89 65 0 30 87 Genetic Engineering

83 235 64 31 2 54 25 Protect the Atmosphere

84 101 27 16 0 22 14 Alternative Energy

85 670 88 69 38 59 84 Official Corruption

86 40 27 16 0 19 12 Bink Failures

87 151 75 42 2 42 36 S&L Prosecutions

88 32 29 21 0 11 26 Crudp Oil Price Trends

89 17 8 2 0 3 7 OPEC Investments

90 75 39 10 0 39 9 Oil & Gas Reserves

91 9 6 3 0 5 5 Acq of Advanced Weapons

92 27 17 6 0 7 9 Military Equip Sales

93 94 65 61 9 61 62 NRA

94 300 76 47 0 57 62 Computer Crime

95 359 77 45 1 63 40 Computer Crime Detection

96 310 86 42 2 23 49 Computer Medical Diag

97 319 59 21 0 20 41 Fiber Optics Appl

98 722 96 67 0 83 84 Fiber Optics Manuf

99 291 99 92 10 91 89 Iran-Contra

100 204 94 84 0 52 89 Controlling High Tech

206
5.0 Analysis of Results

The results from our two TREC runs (Table HI) are summarized below. The proximity

queries (TRWl) scored at or above the median on 28 topics (including three topics which

achieved the best score) and below median for 22 topics. While not bad, our proximity

queries did not perform as well as we'd hoped. Where the proximity queries did poorly,

we attribute this primarily to poor term selection. One such case was Topic 65, Information

Retrieval Systems. We made two errors: first, due to an oversight, one of the manually

entered query terms was overly broad; second, the query author considered database

systems to be "information retrieval systems". We feel that this is a fault of the query

formulation, not of the assessments. If we had checked the training assessments, we would

have eliminated the terms from our query. Any system which relies solely on the topic

statement will run afoul of this problem. Systems which make use of user-supplied

relevance information will achieve better performance. Our results again demonstrate the

inherent problems with basically boolean query formulations. Our efforts at using

proximity to soften the boolean were not sufficient to overcome this weakness.

Run High Above Med Median Below Med Low 11 -pt avg

TRWl 3 19 6 22 0 0.2525

TRW2 5 27 5 13 0 0.3459

The statistical queries (TRW2) did much better, scoring at or above the median on 37 of the

topics. The 1 1-pt average and total relevant documents retrieved figures were excellent and

are close to the best academic groups. The adaptations to run the statistical queries on the

PDF hardware evidently did not hurt performance. We again observed that the dominant

factor in achieving good performance is proper term selection. The details of the term

weight calculations didn't seem to make much difference except to influence which terms were selected. We tried a number of different schemes for generating term weights

including using various statistical parameters, log weighted coefficients, and converting

terms present in all sample documents to boolean ANDs in the query. For a given set of

terms, we did not find much difference in performance between these schemes. The

scheme used for TRW2 was one of the simpler ones we tried. We were also interested in evaluating the use of phrases and special features as additional

terms in our statistical queries. They seemed to help, but not dramatically. This was a disappointment. Lx)oking at the results topic by topic however, we observed a lot of

variation. For some topics, the addition of a key phrase or special feature helped a great

deal. This indicates that use of phrases and special features has promise for improving

performance, but that we just have not learned how and when to employ them. For

example, our term weighting scheme this year didn't account for term interdependence.

Particularly when we start mixing single word terms with phrases and special features that

contain those same terms, it would seem the algorithm could be improved by explicidy

accounting for this redundancy.

207
6.0 Conclusions and Future Plans

Overall, our results for TREC-2 are encouraging. We were successfull in adapting the PDF

hardware to running a soft statistical information retrieval algorithm. Not only did the FDF

run all the final searches with no significant post-processing, we also found it a useful tool

for experimenting with different proximity windows and deriving database frequencies for

phrases and special features. We plan to continue our work and to participate in TREC-III

next year. The lessons we learned from TREC-I have proven very valuable, and so too

should the lessons of TREC-II. For TREC-III, we will continue to examine the range of

available query formulation, execution and evaluation models, with an eye to adapting

those models for use with the FDF search hardware. We are looking at methods to use the

raw horsepower of the FDF to expand existing techniques in ways which had previously

been considered too computationally expensive.

We are also struck by the observation that different query generation techniques are

effective on different topics. A system that could execute a range of methods might be able

to match the query generation approach to the topic type. We plan to consider possible

schemes for characterizing the topic in advance so that the system might be able to use the

best method.

208
Knowledge-Based Searching with TOPIC®

John W. Lehman, Clifford A. Reid, et al.

Verity, Inc.

1550 Plymouth Street,

Mountain View, CA 94043

(415) 960-7620 / jlehman@verity.com

1 . OBJECTIVE OF VERITY'S TREC-2

EXPERIMENTS

Verity, Inc. is the first major commercial product

participant in TREC. Verity's product is TOPIC®.

Verity participated in TREC-2 as a Category A Site. This participation was Verity's first TREC, and we

encountered many of the logistical problems of other

sites in their TREC-1 experience.

Topic's search users wish to understand the search result quality to expect in their personal searches on their (large) collections. Verity also expects to obtain insights

for future product improvements.

Topic is a mature commercial-off-the-shelf manual text search program combining the results of human

expertise with a powerful search expression language and

fast search algorithms. Topic's installations use manually or semi-automatically developed libraries of

searches (topics) , which are instances of the search expression language and which are supplied to all users.

Verity begins its TREC experiments with a gathering of

"ground truth" regarding unaided adhoc end user search

result quality. Future experiments will incorporate

predefined searches (topics') and other Topic search aids to determine their level of improvement/impact on search

result quality.

2 . TOPIC SEARCH APPROACH

The Topic philosophy: Domain knowledge, both

descriptive and content-based, using constructs

specifically designed to discriminate between fiill text material, is the only way to consistently obtain high

recall/precision on large heterogeneous collections. Search result quality may be enhanced by the employment of collection-specific statistics to locate additional domain-relevant terminology. Searches are

repeated and subject-matter expertise is a scarce resource. The problem that Topic addresses is the effective use ofa human's time in analyzing search results to locate the

preponderance of relevant details in thefewest possible

documents, and therefore the smallest possible elapsed

time.

2 . 1 TOPIC KNOWLEDGE

REPRESENTATION

The Topic product employs several approaches to individual term search, organized by a rule-based, or concept-based, approach to search term aggregation. In Topic, the search focus is the topic , (concept, notion,

idea, or subject), and the topic is the user-specified "smart" description of all of the evidence "about" or "of

the topic as it (the evidence) would be found in text documents.

2.1.1 TOPIC INDICIES

The Topic product Une catalogs and indexes both fielded

(structured) data, and full-text. Topic automatically

extracts structured data (such as title, author, etc.) into searchable fields, using a lexical analyzer. Fielded data is searchable separately or in combination with full-text.

Indexes on the full-text are (for all non-stopped characters and strings):

-word/string

-stemmed word (morphological variant)

-soundex (phonetic spelUng variety)

-statistically correlated terms (called the suggestion index)

-typographical error index

-thesaurus -wildcard (universal character/group expansion)

An index on all values (choices) for fielded data is also produced.

2.1.2 TOPIC SEARCH RULES

Search rules consist of relational comparisons to field values, exact or fuzzy matches on full-text search terms,

209
aggregated by boolean and evidential reasoning operators

and point value uncertainty at the term level (each piece

of evidence has a strength/uncertainty attached to its predictability of its parent concept).

Topic provides search rule management functions to support the creation, repeated use, modification, sharing

and display of one or more libraries of related search

rules. The search rule libraries are themselves searchable,

including text annotations of the rules. Search rules are

interactive queries, automatic queries, and a training

mechanism for the installation's domains. ^ A search rule definition may include several thousand

pieces of evidence in over one hundred levels of detail. One search rule library may contain twenty thousand

rules. Search rules (topics) are named, and a reference to the name in a search expression inherits all lower levels of evidence. Any query which includes a search rule name will automatically receive the full definition of the

rule in the search. The lowest level of evidence is the

text expression. Search rules may be composed of other named search rules.

Search rules appear as an alphabetical list of topic names, an indented outline showing the levels of rules, or a graphical "family tree" display of rules and their parents/children, including evidence combination

operators and evidence "weights". Searches may be

executed directly from any node (name) in the search rule family. A topic search rule graphic display example

appears in Figure 1.

The search rule syntax consists of an exact or fuzzy

match (pattern match) capability for individual terms

(case sensitive); a boolean combination (and (all), or

(any), not), of terms; dual direction, nested, grammatical

(paragraph, sentence, phrase) proximity operators; a

relative (fuzzy) proximity operator for two or more

terms, an evidence aggregation operator (accrue) for both

full-text and structured field data, and inexact match

techniques as follows:

1. wildcard expressions for term expansion; single

character, character group, or character class

2. soundex (first letter common) expressions for morphological term expansion

3. source language-specific stemming (morphological

variants) expressions for term expansion

4. typographical expressions for term expansion (n- character infidelity to search term)

5. multi-direction thesaurus (user-modifiable) for term

expansion

6. suggestion (statistical correlation) for term

expansion

7. evidence appearing in a field value, or as the field value (contains, matches, substring, starts, ends).

Each of the above inexact match techniques may be

executed automatically. Negative evidence may be

applied on a term-by~term basis with any operator. The

structured field data types are character, number and date. Date arithmetic is provided, as well as relative date expressions such as "yesterday"

, "today" etc.

2.1.3 SEARCH RESULT RANKING

Results of searches are relevance ranked lists of

documents, with displayed titles or other descriptive

information. The numeric score, and the accompanying

rank, are the result of a best fit comparison of the full- text document and descriptor content and the search rule evidence. The ranking is subject to an optional

threshold, used primarily to limit output, but the

threshold may be used to describe search recall and

precision. The relevance threshold is always used in dissemination/notification.

Evidence consists of terms, operators (syntax) and the numeric strength of the relationship between the evidence and its (next higher level) search rule. The

evidence may be aggregated or evaluated with boolean

operators. Aggregation involves giving relevance score

credit for each piece of evidence found (breadth of

evidence first). As each level is evaluated in a search rule

(tree), potential document score modification occurs

(since successive levels may be weighted evidence for

their next broader concept). The scoring of an individual term may include a frequency-of-occurrence factor (a normalized concentration factor) , a less powerful scoring

factor than the absolute presence of the evidence in the document. A document score explanation function is included.

2 . 2 AGGREGATE SEARCH

FUNCTIONS

Searches may iterate on the results of the previous

search.

Any search may be named/saved along with its results manipulation criteria (sorting by fields, grouping) for

later execution. Any search criteria may be interactively

defined as a logical view of the collection, which then

provides many alternative search universes for the user

population. All Topic activities are audited. A search

which supports discretionary access control may be

transparently appended to any users search.

210
Figure 1 TOPIC Search Rule and Result

3 TOPIC

File Edit View Query Navigate Launch Window F1=Help

LAW

p 0.50 < <Many> <Stem> law

- 0.50 < <Many> <Stem> court

- 0.50 < <Many> <Stem> suit - 0.50 < <Many> <Stem> plaintiffs

law <Accrue><^ —0.50<^<Many> <Stem> regulations

- 0.50 < <Many> <Stem> justice

I- 0.50 ( <Many> <Stem> lawyer

I- 0.50 < courtroom <Accrue><^ —0.50<4<Many> <Stem> judge

-0.50<4<Many> <Stem>jury

0.97

0.93

0.93

0.93

0.89

0.89

0.88

0.88

1 3-Feb-90

1 3-Feb-91

1 4-Feb-90

06-Mar-90

1 3-Feb-91

21-Feb-90

01-Mar-90

05-Mar-90

Law - Legal Beat: Monsanto Is Cleared in S600 M

Candela Announces Patent Suit

Law - Legal Beat: Suits Prompt FDA Investigation

Business World: Prop. 65 Populism Is on the Pro

North Carolina Bar Association Supports Judicial

High Court Declines to Hear Challenge On Line B

Justice Aides Say Chances for Winning Big Awar

Law- Washington Docket: Poindexter Case, HUD

211
2.3 USER INTERFACE TO SEARCH

Every search is automatically configured into a rule. The

simplest search is a list of terms, which may be entered

at the keyboard, selected from displayed document(s)

content, or selected from lists of terms. This list is automatically enhanced by term expansion, expansion to existing named rules whenever the rule name appears in the search expression, and evidence aggregation. Searches

involving structured fields are generally addressed by a form interface, which aggregates field and full-text content. Any list of terms, rule-names, or extensions

such as thesaurus/soundex may be used to initiate a

search or add to a search expression.

2.4 SEARCH RESULT ANALYSIS AIDS

The Topic philosophy of minimizing the elapsed time to obtain the necessary relevant details that constitute an answer or support a decision necessitates analysis aids beyond the search composition and result list display.

The Topic result list may be browsed (page, result number etc.). A document selected for display produces

the full text display with all search evidence highlighted

(e.g. in reverse video or color). The display may be the native form of the document, which for most of today's

collections means a marked-up format with usefiil user guidance in the markup itself (e.g. sections, paragraph

headings etc.). The user may choose to browse or to move directly to the first/next/previous occurrence of a search term in the document. Similarly, the user may

move through the document using various document

enhancements such as hypertext links, may follow

hypertext links to other documents, including graphics

and other media. Previously generated annotations are available for browsing. Queries or other applications

may be linked to document content. A specific search term (not necessary to be a part of original search) may

be used as a browsing aid to the document.

2.5 SECURITY

Users may be prevented from accessing information via operating system permissions, and built-in access

controls, including discretionary. The product processes have been certified at system high in many installations,

and some sponsors have applied for MLS certifications based upon the delivered product.

2.6 DATA ARCHITECTURE/

PERFORMANCE/ CONHGURATION

Topic enables the logical division of a collection of

documents into "partitions", which are document

descriptions and indexing data about the arbitrary/ intentional subset. Partition size, purpose and

characteristics are under the application administrator's

control. The raw documents are not "owned" by the Topic application. Topic will produce indicies which are approximately 70% of the size of the native text size

(the TREC-2 index size was approximately 50%). This

includes fielded, word, and subject (rule evidence) level

indicies.

The partition data is platform-independent (i.e. the documents and their associated partitions may be

moved/accessed from any Topic platform.

Searches may be performed on the served desktop, on a host or both.

Normal performance on a personal computer is in the thousands of document-rule nodes per second, up to many tens of thousands of nodes per second on current

workstations. The search rule low level evidence is contained in a size/speed-optimized index (topics), which

is essential to rapid response on complex rules. This

index is automatically modified each time topic evidence

is added, so the word positional information is searched

only on the first use of the term. The topics index

normalizes document size so that all search response

times are predictable. Partitions enable incremental

(ranked) results, guaranteeing few-second time-to-firstresult, regardless of the size of the collection. The

response characteristic which Topic optimizes is the

time-to-first-meaningful-result. The rule evidence index

may be centralized or distributed, and when distributed, it provides the ability to produce a ranked results list with

a minimum of network access.

Integration with third party components is available from the end user interface, or shared libraries. The

program provides logical links between document-image,

document-document, document-annotation, documentsearch request. Some links may be automatically

determined at indexing time (image, cross-reference).

The structured field values may be entered interactively, or filled automatically from a lexical analyzer. The

program provides an enduser process interface between

scanning, OCR/ICR and indexing.

3 . THE TREC EXPERIMENTS

3 . 1 DATA PREPARATION

The TREC-2 texts data preparation processing was

performed on a Sun SPARC 10 (UNIX 4.1.3). Cataloguing and indexing was performed at the rate of

approximately 100 Mbytes per hour. This process

included the automatic extraction of 10 fields from the ASCn content. Partitions were set at 8000 documents

for all data. There were no processing errors.

212
No markup language (SGML) interpreter was used

during data preparation, and the optional alphabetical

word list (used only for display) and typographical error index (used almost exclusively for OCR'd data) were not employed. Special indicies such as correlated terms, and

paragraph/sentence positioning were not produced. As

the fuzzy proximity operator was used in the tests, only a word position index was produced. No document was

divided into logical or arbitrary sections for processing or search result enhancement, although that approach is used in virtually all non-newswire Verity installations. The purpose of logical division (a forerunner of the

intelligence available in a standard markup language) is to create domain-specific logical documents, and

therefore to reduce the impact of larger, multi-subject

documents on results (they would appear in search

results simply because of their breadth of words).

3.2 TOPIC CONSTRUCTION

Verity personnel manually constructed the search rules from the subject area descriptions and the training data. No rule developer was identified or chosen as a subject matter expert, and for certain of the contributors, this was their initial interface with using Topic. [Search rule

libraries are created by approximately 6% of Topic's user population and the remainder of Topic's users employ

the topics developed by others]. On the average, the TREC-2 volunteers were considered novices on the Topic product, particularly the search rule development

area. Volunteers were not encouraged to use specific features of the product, and in at least one case, inadequate communication produced potentially inaccurate search expectations. As search rules were

interactively developed, the rule evidence was

automatically indexed for repeated use of the rule. The

twenty volunteers each produced between 3 and 8 retrospective and routing queries. The range in time

spent on individual query development, and result production was from fifteen minutes to eight hours, over

a several week period. The average time to produce the TREC-2 result, obtained from interviewing the

volunteers, was approximately one hour.

3 . 3 EXPERIMENT PERFORMANCE

Typical response time performance on the searches was

two seconds per 8000-document partition, or approximately two minutes to search the entire

collection. A single term, indexed as rule evidence, was

used to search the entire collection, and the 1.1 million

document collection was searched in 21 seconds.

3 .4 ANALYSIS OF OFHCIAL RESULTS

The post hoc analysis of Topic's TREC-2 results generally found that the Topic system performed well. When compared with other manual systems, the scores

are amongst the best. I the few cases where Topic

appeared to fail, we have generally been able to identify

easily correctable deficiencies, that, had they been noticed

during the experiment proper, would have resulted in superior performance by Topic in TREC-2.

Based on our analysis, we believe that the prospects for TREC-3 look very bright.

Our analysis of selected results from our TREC-2

submissions focuses mainly on the "failure cases" since

these are most likely to give us insights in how to improve Topics (and users) performance in future TREC

experiments. This also allows us to investigate whether

there are any fundamental issues with using Topic to model the information need statements used in TREC.

We analyzed two routing and three ad-hoc topics in

detail. Our summary follows.

The following general observations applied to all searches:

-Adhoc searches were submitted against all three disks,

which produced poorer quality results generally, as documents from disc three appeared in some search

results.^

-Field value evidence was not used, and in some

domains/subject areas, domain knowledge about the sources of information would favor (rank higher) sources with the appropriate use of terminology, (e.g. business

sources about financial performance, or foreign datelines have higher likelihood of describing foreign prominent

persons/activity, as in topic's 66 or 121)

-The queries which used attempted to use nomenclature

with hyphens (e.g. M-1) failed to return an exact match

as the hyphen was not included as an indexed character.

-The fuzzy proximity ( near) operator was undocumented,

only one volunteer used it and other users expected

sentence / paragraph proximity in their searches. The

index did not contain sentence / paragraph positional

data, and all uses of sentence or paragraph operators

produced erroneous results because the search arbitrarily

assigned sentence and paragraph boundaries.

For routing queries, the score threshold was set to zero; any document containing evidence entered the routing

result list. ^Reprocessing the adhoc searches against only disks 1 and

*2 produced a numberic result improvement of 0-70

percent, with a *few changes from under the median to over

the median.

213
3.4.1 ROUTING TOPICS

Overall, Topic's performance on the routing topics was

rather good. We count that 21 of the 50 results were at or above median, and three were actually the bet score. Most of the other results were on the low side of the median. The relevant comparison to the median is summarized in Figure 2. The exceptions were topics 66,

67, 69, 74, 90 and 91, for which the Topic search used

could be said to have failed. Several of these were

straightforwardly explained. For example, in the case of

topic 67 the wrong results were submitted. Our

independent scoring of the correct results set would give

the Topic search below median score. For topic 69 there was in fact only one relevant document, but, at least in our reading of the definition, this seems to be a false positive. In the case of topics 90 and 91 the Topic

search definitions were, in our opinion, over-constrained.

Further, in the case of topic 91 an index creation decision prevented a quite reasonable Topic definition from performing as well as it could.^ The other two

topics are of more interest.

No clear pattern emerged between the type of search although, in the routing augmentation category, the Topic performance was well above the median on 20 of

33 searches.

3.4.1.1 ROUTING TOPIC 66

A relevant document for this topic is one that identifies a type of natural language processing technology that is being developed or marketed in the United States. The

original definition of the Topic is basically a conjunction (AND) of a natural language concept and a products/technology concept.

Performance was very poor, viz: Relevant = 86

ReLret = 1 R-Precision = 0.0000

Inspection of the Topic revealed that one of the conjuncts (the products/technology concept) had a weight

of 0.05 - thus effectively limiting the range of scores

that Topic could produce to be in an extremely narrow

range.

"^This topic is about the acquisition of advanced weapons

by the U.S. Army. One of the weapons systems mentioned

in the information need statement is the M-1 tank. This was

included in the Topic definition as the word "M-1"; but

since the "-" symbol was interpreted as with like space at database build time, there was no possibility of retrieving

documents based on "M-l" as a word.

We changed the 0.05 to 0.5 and produced the following:

Relevant = 86

Rel_ret = 44

R-Precision = 0.2442

which is a median result. We concluded that for Topics to be effective we need to ensure a sufficient range of scores to give us the discrimination needed for the TREC scoring algorithm.

3.4.1.2 ROUTING TOPIC 74

A relevant document for this topic is one that cites an

instance in which the U.S. Government propounds two

conflicting or opposing policies. The routing task is complicated because this conflict may not necessarily be

mentioned in the same document.

In our opinion, this is a case where no amount of

sophistication in Topic construction would enable Topic

to do very Well. The information need is simply outside

the scope of a retrieval system that uses non-NLP

techniques. The best one could hope for is to model a document that talks about the meta-idea of conflict (i.e., find documents that talk about the US having conflicting

policies, rather than documents that reference the specific

conflicting policy). This is, in fact, what was done in the original submission. The results were: Relevant = 323

ReLret = 18 R-Precision = 0.0464

which is, of course, rather poor.

The original statement of need actually mentions three examples of conflicting policies so, as an experiment, we ran the following query:

* <Many><Stem>

/wordtext = "tobacco"

* <Many><Stem>

/wordtext = "pesticide"

* <Many><Phrase>

* <Many><Stem>

/wordtext = "infant"

* <Many><Stem>

/wordtext = "formula"

that is, just an ACCRUE of "tobacco", "pesticide" and

"infant formula" (which the modification that the <Stem> and <Many> operators produce.

This gave the following results: Relevant = 323

ReLret = 107

R-Precision = 0.2660

which puts the score slightly above median. We expect

that most TREC-2 participant sites probably did just

this, and those that did much better than median found some other specific examples of a conflicting policy and

modeled these in their routing queries.

214
Figure 2

TOPIC2 Relevant vs. Median - Routing Topics

51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

215
3.4.1.3 TOPIC 67

Our analysis located weak topic formulation examples,

such as query 67, illustrated in Figure 4. In this query, a

set of optional, auxiliary evidence was "ANDed" with a small set of required evidence. The weight, or strength

assigned to the auxiliary evidence was .05, which means

that if all auxiliary terms were located, the highest

possible score for a document would be .05, severely

limiting the range of scores, and thus the occurrence of

random false hits in the top 1000.

To make a cosmetic improvement, only the value of the

auxiliary evidence node was changed, to a value of .5, as shown in Figure 5. This change alone brought the Topic

relevant document count to the median.

3.4.2 AD HOC TOPICS

Overall, Verity's performance on the ad hoc topics was

adequate. Performance was poorer than on the routing

topics, but this is to be expected since there was less time available to build the Topics and no ground truth against which to test the Topic trees. The relevant comparison to the median is summarized in Figure 3. We count that 13 of the 50 results are at or above

median. In contrast thought, there were only two

outright failures here, topics 124 and 139. We did not look at topic 139, but topic 124 involves searching for documents that discuss innovative approaches to cancer

therapy that do not involve any of the traditional treatments. This is a very hard topic because nearly all mentions of the innovative treatments are in the context of discussion of traditional therapies The approach

adopted by Verity of simply looking for documents that talk about innovative treatment produces a large number

of false hits (giving poor precision), and since there is an

artificial cut-off at 1000 documents in the TREC

experiments, this model also produces poor recall. We

do not see an obvious solution to this. We picked three ad hoc topics to analyze in detail.

3.4.2. 1 AD HOC TOPIC 109

A relevant document for this topic simply needs to mention one of a list of six companies given in the information need statement. A simple Topic that is the disjunction (OR) of the company names should be all that is needed here. However, the official result is: Relevant = 742

Rel_ret = 192

R-Precision = 0.2588

which is well below median, furthermore, given the

simplicity of the topic, this is surprisingly low recall.

Examination of the official Topic showed that company

acronyms we used for three of the companies (i.e., 3M,

OTC, ISI) were given equal weight to the fully spelled out company names. A cursory review of the original

hit list showed that ISI was a poor choice since it has

multiple interpretations. Less important, but for the same reason, OTC is a poor choice in the Wall Street Journal corpus since it can mean "over the counter", and

in the DOE corpus 3M is part of a designator for a

particular particle accelerator and is also used as an

abbreviation for "three meters". We modified the Topic by eliminating the ISI acronym

and by giving OTC and 3M reduced weights. This

produced the following:

Relevant = 742

ReLret = 480

R-Precision = 0.5512

which would have been the best score.

An interesting note here is that original and modified

Topics had perfect precision and recall for the first 100

documents. Our conclusion is that this indeed was an

easy topic - the false hits produced by ISI were what

impacted Topics score.

3.4.2.2 AD HOC TOPIC 121

A relevant document for this document had to mention

the death of a prominent U.S. citizen due to an identified form of cancer.

This is an interesting topic consisting of two major

components - the idea of a prominent citizen, and the idea of a specific cancer.

In the official Topic, prominence was modeled using a number of words that indicate prominence (e.g., "prominent", "celebrity") together with words that indicate prominent roles (e.g., "Nobel Prize", "actor",

"actress"). Cancer death was modeled by various

combinations of death words (e.g., "death", "died") and

cancer words (e.g., "cancer", "tumor", "leukemia"). The

official score was: Relevant = 55

ReLret = 27

R-Precision = 0.1455

which, while not good in absolute terms, was well above the median.

We observed two problems with this definition. First, it uses generic cancer terms rather than the specific cancer

types required by the information need statement. So, we made all the cancer terms specific by using a list of common cancers (e.g., lung cancer, breast cancer, stomach cancer, etc.) . We made no attempt to make

216
Figure 3

TOPIC2 Relevant vs. Median - Adhoc Topics

m

^^^^^ D0** a

DD

01 02 03 04 05 06 07 08 09 10 1 1 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

217
Figure 4

Example of Poorly Specified Search

Routing Query #66

Topic

Figure 5

Poorly Specified Search - Cosmetic Repair

Routing Query #66

"Natural Language"

Topic

218
this list exhaustive. This produced the following

results: Relevant = 55

Rel_ret= 17 R-Precision = 0.2182

Thus we reduced the recall, but increased the precision.

Presumably by adding more specific cancers (or at least the ones that statistically are most common) we could

have improved the recall here.

The second problem is more severe though. It appears impossible to build any kind of model that would allow

us to determine, with any kind of confidence, that the person who has died is a US citizen. In our revised

results list we find many prominent persons who died of

a named cancer but who are not US citizens (e.g., the Venezuelan Ambassador).

In addition, the notion of prominence is also hard to capture. Of course, we might argue that anyone who's

obituary is on the wire service is prominent by

definition! Be that as it may, we observed a number of

documents that we did not retrieve because we had not included the specific prominent role indicator in our

Topic. Thus we added the following roles words - "author", "poet", "writer", "artist", "painter" - to the Topic and got the following results: Relevant = 55

Rel.ret = 33

R-Precision - .0909

Thus we improved the recall but at the expense of the precision again. Notice that we still have not included

any business or government roles, which presumably

would help retrieve the relevant documents in the WSJ

corpus.

Our conclusion, is that this is a significant challenge for Topic, and all other system. The citizenship question

often cannot be resolved by reference to the text alone,

and we see no alternative but accept the false hits. Prominence is also difficult, but could conceivably be

approached by an extensive list of prominence and role, words. The specific cancer seems tractable since there are only a finite number of cancers and just a small set of those are common.

3.4.2.3 AD HOC TOPIC 133

A relevant document for this topic must describe some

design feature of the Hubble Space Telescope, but must

not report of the launch activity itself nor the Hubble

Constant or Edwin Hubble.

The official Topic was essentially a simple structure of

the form: Hubble Space Telescope and not launch and

not Edwin Hubble. This gave the following results: Relevant = 80

ReLret = 29

R-Precision = 0.3625

which is surprisingly poor given the apparent simplicity

of the topic.

Analysis of the behavior of the negation function in Topic shows that it is too restrictive, and so we

eliminated the negated concepts leaving just the phrase

"Hubble Space Telescope". Using this as the query

gave: Relevant = 80

ReLret = 78

R-Precision = 0.6000

which would have been above median and close to best.

Adding as disjuncts (OR) the words "Hubble" and "HST"

gave: Relevant = 80

ReLret = 79

R-Precision = 0.6000

that is we retrieved one extra relevant document with no

decrease in precision.

We conclude that although the information need

statement is careful to spell out the cases where the document will be non-relevant, the TREC corpus has

few documents where these conditions apply, so that a simple query performs very well. This is presumably

the approach most sites took.

4 . FINAL OBSERVATIONS FROM

TREC-2

The TREC-2 topic descriptions, particularly the ad hoc

topics, exceed the level of domain knowledge available

to most users of heterogeneous document collections.

Most Topic (content-based) search operational users are driven by time pressures to locate/summarize the most

relevant details in the fewest possible documents. The

exhaustive search result analysis implied by examining

hundreds of relevant documents will not be addressed in most user environments; our experience is that ten to

thirty documents is the level of search result analysis

performed by a user (unless significant duphcation of

material occurs earlier, which would reduce the number

of documents actually analyzed). Ergonomically, high

precision in the first (10, 20. ..50) documents is more

likely to keep users attracted than high recall at much

larger counts.

Although we have yet to perform any analysis of

duplicate information on the TREC2 results, our belief

is that duplicate data is plentiful in the TREC2 "relevant

lists", and that the reading of duphcate data by the human user will cause the result analysis to be

(prematurely) terminated. We are certain that, unless summarization is performed,

the relevant search results on most topics are too numerous to warrant user attention. It would seem

219
reasonable to examine, at least for selected topics,

whether the first ten/best ten documents address the domain well from a domain "precision/recall"

perspective. To the extent that the domain is well served

in a few representative documents, the coverage in the representative documents may be a "better" answer for the user than the numerical count of the number relevant

in the first 1000. We recommend adding a measurement

of the coverage of the domain as the first ten/thirty/n

results documents are examined.

220
Appendix A

COMPANY AND PRODUCT SUMMARY

Topic is a commercial off the shelf software product line available from Verity, Inc. Topic search technology is a commercial adaptation of ideas extracted from the research of Tong, McCune et. al., in Rule-Based

Information Retrieval, which was sponsored by the U.S.

Intelligence Community. Topic supports cataloguing,

indexing and retrospective search of fixed collections,

automatic search of newly indexed documents according

to (user) predefined search rules (profiles), and

dissemination/notification based upon satisfied search

rules. Documents may be batched for indexing/profiling, or processed automatically as they arrive.

The Verity, Inc. market presence in content-based text search/retrieval is described in the Delphi, Inc. 1992

Industry Summary. The Verity Topic product line is considered to have in excess of a ten percent share of the market in commercial-off-the-shelf content-based

search/retrieval products for personal computer to minicomputer environments.

Verity was founded in April 1988. The Topic product was first licensed and installed by the U.S. Air Force in June 1987. Verity currently has over 650 installafions and some 30,000 users. Many thousands of persons have

received training from Verity on the Topic products.

Approximately one-third of Verity's installed base uses an event-driven or batch automatic-search-notification

function. Many organizations use the routing mechanism for users who are unable to compose the (appropriate) queries, but

require the expert's result quality.

The Topic product line supports nearly twenty varieties of the UNIX operating environment, VMS, OS2, DOS

and Macintosh. The product operates on data stored in the filesystem or in any SQL-based data base

management system. The product as shipped supports over twenty formats of native data (markup languages),

and provides the ability to insert local/third party markup

language interpreters as required. A document in Topic is logical, and may be a file, subfile or any logical

decomposition of a physical native document.

The Topic end user (search) product is available in MSWindows, Presentation Manager, X-Windows-Motif,

Macintosh, and character (keyboard/terminal) interface

styles. There is a 4GL-like command interpreter

language for rapid application development and remote command line interactive index/search. There is an

Application Program Interface (C- library) to all Topic

functions for embedded applications.

221

On Expanding Query Vectors with Lexically Related Words

Ellen M. Voorhees

Siemens Corporate Research, Inc. 755 College Road East

Princeton, NJ 08540

ellen@learning.scr.siemens.com

Abstract

Experiments performed on small collections suggest

that expanding query vectors with words that are

lexically related to the original query words can improve retrieval effectiveness. Prior experiments using

WordNet to automatically expand vectors in the large

TREC-1 collection were inconclusive regarding effec- tiveness gains from lexically related words since any

such effects were dominated by the choice of words to expand. This paper specifically investigates the effect of

expansion by selecting query concepts to be expanded

by hand. Concepts are represented by WordNet syn- onym sets and are expanded by following the typed

links included in WordNet. Experimental results suggest that this query expansion technique makes little difference in retrieval effectiveness within the TREC en- vironment, presumably because the TREC topic state- ments provide such a rich description of the information

being sought.

1 Introduction

The IR group at Siemens Corporate Research is in- vestigating how concept spaces — data structures that

define semantic relationships among ideas — can be

used to improve retrieval effectiveness in systems de- signed to satisfy large-scale information needs. As part

of this research, we expanded document and query vectors automatically using selected synonyms of original text words for TREC-1 [5]. The retrieval results indicated that this expansion technique improved the

performance of some queries, but degraded the perfor- mance of other queries. We concluded that improving

the consistency of the method would require both a better method for determining the important concepts of a text and a better method for determinmg the correct sense of an ambiguous word.

We took TREC-2 as an opportunity to investigate

the effectiveness of vector expansion when good concepts are chosen to be expanded. As in TREC-1, query

vectors were expanded using WordNet synonym sets.

However, the synonym sets associated with each query

were selected manually (by the author). These results therefore represent an upper-bound on the effectiveness to be expected from a completely automatic expansion

process. The results of the TREC-2 evaluation indicate that

the query expansion procedure used does not significantly affect retrieval performance even when important concepts are identified by hand. Some expanded

queries are more effective than their unexpanded counterparts, but for other queries the unexpanded version

is more effective. In either case, the effectiveness differ- ence between the two versions is seldom large. Further

testing suggests that more extreme expansion proce- dures can cause larger differences in retrieval performance, but the net effect over a set of queries is de- graded performance compared to no expansion at all. The remainder of the paper discusses the experiments

in detail. The next section describes the retrieval environment, including a description of WordNet. Section 3 provides evaluation results for both the official TREC-2

runs and some additional supporting runs. The final section explores the issue of why the expansion fails to improve retrieval performance.

2 The Retrieval Environment

The expansion procedure used in this work relies heavily on the information recorded in WordNet,

a manually-constructed lexical system developed by

George Miller and his colleagues at the Cognitive Sci- ence Laboratory at Princeton University [4]. WordNet's basic object is a set of strict synonyms, called a synset. Synsets are organized by the lexical rela- tions defined on them, which differ depending on part

of speech. For nouns, the only part of WordNet used in this study, the lexical relations include antonymy, hypernymy/hyponymy {is-a relation) and three different meronym/holonym {part-of) relations. The is-a rela- tion is the dominant relationship, and organizes the

223
synsets into a set of approximately ten hierarchies-^. Figure 1 shows a piece of WordNet. The figure contains all the ancestors and descendents as defined by

the is-a relation for the six senses of the noun swing.

Also shown is that one of the senses, a child's toy, is part- of a. playground.

Given a synset, there is a wide choice of words to add to a query vector — one can add only the syn- onyms within the synset, or all descendents in the is-a hierarchy, or all words in synsets one link away from

the original synset regardless of link type, etc. One of

the goals of this work is to discover which such strate- gies are effective. Wang et al. found expanding vectors from relational thesauri to be effective [6], but based

those conclusions on experiments performed on one

small collection. Experiments we performed as part of our TREC-1 work showed showed serious degradation

when anything other than synonyms were used in the

expansion — but the TREC-1 results were dominated

by the problem of finding good synsets to expand. This

work examines the effectiveness of the different relation

types assuming good synsets are used as the basis. Siemens' official TREC-2 runs consist of one rout- ing run (topics 51-100 against the documents on disk

three) and two ad hoc runs (topics 101-150 against the

documents on the first two disks). All of the runs are manual since the input text of the topics was modified

by hand. There are two types of modifications: parts

of the topic statement that explicitly list things that

are not relevant were removed, and synsets containing nouns germane to the topic statement were added as a new section of the topic text. Document text was in- dexed completely automatically (once the errors were

fixed^) using the standard SMART indexing routines [1] (i.e., tokenization, stop word removal, and stemming).

In general, only the "text" fields of the documents were

indexed. For example, only the title, abstract, detailed

claims, claims, and design claims sections were indexed

for the patent sub collection. The manually assigned

keywords included in some of the Ziff documents were

not used, nor were the photograph captions of the San

Jose Mercury collection. The goal in selecting synsets to be included in a topic

statement was to pick synsets that emphasized important concepts of the topic. One aspect of the problem is sense resolution: selecting the synset that contains the correct sense of an ambiguous original topic

word. However, since one purpose of the experiments

^The actucd structtire is not quite a hieraxchy since a few

synsets have more than one parent. ^There were seven errors total in files patn_014 and patnJOSl

that were not on the official hst, but caused the files to not con- form to the patent collection's readme file. These errors — miss- ing '/TEXT' tags , 'TEXT' tags preceding 'OREF' tags, and the

like — were also fixed majiually.

is to investigate how effective lexical relations are in expanding queries assuming good starting concepts, I did not restrict myself to adding only synsets that contain some original topic word. For example, topic 93

asks for information about the support of tke NRA

and never mentions the word gun. Nevertheless, I believed gun to be an important concept of the topic and

added the synset containing gun meaning "a weapon

that discharges a missile from a metal tube or barrel"

to the topic. {Rifle, a word that does appear in the

topic statement, is a grandchild of this synset in WordNet, with the intervening synset being {firearm, piece,

smaU-arm}). Synset selection was also influenced by

the fact that these synsets would be used to expand

the query. Early experiments demonstrated that ex- pansion worked poorly when synsets with very many

children in the is-a hierarchy (e.g. couniry) were used,

so those synsets were avoided. Furthermore, when se- lecting one sense among the different senses in WordNet

was difficult, I frequently used the words related to the

synsets as a way of making a decision. Figure 2 shows

the original text of topic 93 and the synsets that were added to it. Some topics contained important concepts that had

no corresponding synset. Occasionally, the missing

synset was a gap in WordNet; for example, toxic waste,

genetic engineering, and sanctions meaning economic

disciplinary measures are not in version 1.3 of WordNet.

More often, the important concept was a proper noun

or highly technical term that one wouldn't expect to be

in WordNet. NRA oi National Rifle Association, for example, is an important concept for topic 93 but does

not occur in WordNet. Nothing was added to the topic

texts for concepts that lacked corresponding synsets in these experiments, although making some provision for them would improve retrieval performance.

Once the text of the topics is annotated with synsets,

the remainder of the processing is automatic. Selected

fields of the topic statements (the title, nationality, narrative, factors, description, and concept fields) are in- dexed using the standard SMART routines. The terms

derived from these sections are "original query terms"

.

The expansion procedure is invoked when the synonym

set section is reached. The procedure is controlled by

a set of parameters that specifies for each relation type

included in WordNet the maximum length of a chain

of that type of link that may be followed. A chain

begins at each synset listed in the synset section of

the topic text and may contain only links of a sin- gle type. All synonyms contained within a synset of

the chain are added to the query. Collocations such

as change-ofJocation in Figure 1 are broken into their component words, stop words such as o/are removed,

and the remaining words are stemmed. The word stems

224
abstraction

attribute

act human_activity

human action

entity

attribute activity behavior change

property

ammation

liveliness motion

movement

sound_property maneuver

play

rhythm stroke

swmg

lilt

goit_stroKe

swing

shot golf_shot

diversion

recreation

music

dance music

danceroom_music

ballroom_music

object

inanimate_object

physical_object

thing

change_of_location

motion movement

artifact article artefact

swing

swinging

instrumentality

device

swing

baseball_swing

slice drive

hook

approach

approach_shot

putt

IS-Alink

PART link

mechanical device plaything

toy

swing

jive

playground

chip pitch

chip_shot pitch_shot

Figure 1: Relations defined for the six senses of the noun swing in WordNet.

225
<title> Topic: What Backing Does the National Rifle Association Have?

<desc> Description:

Dociunent must describe or identify supporters of the National Rifle

Association (NRA) , or its assets.

<ncirr> Narrative:

To be relevant, a document must describe or name individuals or organizations who are members of the NRA, or who contribute money to it. A document is also relevant

if it qucintifies the NRA's finsincial assets or identifies ciny other NRA holdings.

<con> Concept ( s )

:

1. National Rifle Association, NRA

2. contributor, member, supporter

3. holdings, assets, fineinces

<syn>

{funds, finance, monetary_resource , cash_in_hajid, pecuniary_resource}

{supporter, protagonist, champion, admirer, booster}

{gun}

Figure 2: Topic 093 and the synonym sets selected for it.

plus a tag indicating the lexical relation through which

the stems are related to the original synset are then

appended to the original query terms. As an example of the expansion process, consider the

synsets for swing shown in Figure 1 . If the synset added

to the topic is the synset containing golfsiroke, and

any number of hyponym (child) links may be traversed,

then the stems of golf, stroke, swing, shot, slice, hook,

drive, putt, approach, chip, and pitch would be added

to the query vector. If hyponym chains are limited to length one, then chip and pitch would not be added.

If the synset added to the topic is the one containing

swing meaning plaything and any link type may be fol- lowed for one link, then the stems of swing, mechanical,

device, plaything, toy, playground, and trapeze would be

added to the query. Stems added through different lexical relations are kept separate using the extended vector space model

introduced by Fox [3]. Each query vector is comprised

of subvectors of different concept types (called ctypes)

where each ctype corresponds to a different lexical re- lation. A query vector potentially has eleven ctypes: one for original query terms, one for synonyms, and

one each for the other relation types contained within

the noun portion of WordNet (each half of a symmetric relation has its own ctype). An original query term

that is a member of a synset selected for that query

appears in both of the respective ctypes. Similarly, a word that is related to a synset through two different relations appears in both ctypes.

The similarity between a document vector D and an

extended query vector Q is computed as the weighted sum of the similarities between D and each of the

query's subvectors:

sim{D,Q)= ai{DQi)

ctype i

where • denotes the inner product of two vectors, Qi

is the ith subvector of Q, and ai, a real number, reflects the importance of ctype i relative to the other

ctypes. Terms in documents vectors are weighted us- ing the Inc weights suggested by Buckley et al. [2]; that

is, the weight of a term is set to 1.0 + ln(i/) where tf is the number of times the term occurs in the document

and is then normalized by the square root of the sum

of the squares of the weights in the vector (cosine nor- malization). Query terms are weighted using ItN: the

log term frequency factor above is multiplied by the term's inverse document frequency, and the weights in the ctype representing original query terms are normalized by the cosine factor. Weights in additional ctypes

are normalized using the length computed for the original terms' ctype. This normalization strategy allows

the original query term weights to be unaffected by the

expansion process and keeps the weights in each ctype

comparable with one another.

226
3 Experiments

The training data for the routing queries was used both

to refine the synsets that were included in the topic text and to select the type of relations used to expand the

query vectors. In some cases a synset that appears

to be a logical choice for a query is nonetheless detri- mental. For example, adding the synset for death to topic 59 (weather fatalities) causes the query to re- trieve far too many articles reporting on deaths that

have no relation to the weather. I produced five diflfer- ent versions of synset-annotated topic texts, although

the differences between versions are not very large. The

version used in the official routing run added an average

of 2.9 synsets to a topic statement, with a minimum of

0 synsets added and a maximum of 6 synsets added.

Of course, the utility of a synset depends in part on how that synset is expanded and the relative weights

given to the different link types (the a's in the similarity function above). Table 1 lists the various combinations that were evaluated using the training data. Four

different expansion strategies were tried: expansion by

synonyms only, expansion by synonyms plus all descendents in the is- a hierarchy, expansion by synonyms plus

parents and all descendents in the is-a hierarchy, and

expansion by synonyms plus any synset directly related

to the given synset (i.e., a chain of length 1 for all link types). Different a values were also investigated. As- suming original query terms are more important than

added terms, the a for the original terms subvector was

set to one and the a for other subvectors varied between

zero and one as shown in Table 1. The most effective run was the one that expanded a query synset by any synset directly related to it and had a = .5 for all added subvectors. Therefore, this strategy was used to produce the official routing queries from the

final version of the annotated text. The scheme added

an average of 24.7 words to a query vector (minimum

0, maximum 70), and an average of 20.2 (0, 66) words

that are not part of the original text. The average number of relevant documents retrieved

at rank 100 for this run is 40.7 and at rank 1000 is 133.3; the mean "average precision" is .2984. In general, the individual query results are at or slightly above

the median, with a few queries significantly below the

median. Of more interest to this study is how the ex- panded queries compare to unexpanded queries. A plot

of average recall versus average precision for these two

runs is given in Figure 3. As can be seen, the effective- ness of the two query sets is very similar. Since there was no way to evaluate the relative ef- fectiveness of different expansion schemes for the ad

hoc queries, the same same expansion scheme as was

used for the official routing run — chains of length one

for any relation type and all a's = .5 — was used for

the ad hoc run. Furthermore, there could be no re- fining of which synsets to add, .so only one version of

synset-annotated text was produced. An average of 2.7 (minimum 0, maximum 6) synonyms Wcis added to an

ad hoc topic text. The expansion process added an av- erage of 17.2 (0, 66) terms and 12.8 (0, 55) terms that

are not part of the original text. Siemens actually submitted two ad hoc runs. The

first was the expanded queries with a's set to 0, a run

that is equivalent to no expansion and is used as a base

case. The second Siemens ad hoc run used the .5 a values. A plot of the effectiveness of the two ad hoc runs

is given in Figure 4. The differences in effectiveness between unexpanded and expanded queries is even smaller

for the ad hoc queries than it is for the routing queries.

The average number of relevant documents retrieved at rank 100 is 46.9 for both the unexpanded and expanded

queries. The average number of relevant documents re- trieved at rank 1000 is 161.4 for the unexpanded queries

and 161.3 for the expanded queries. The mean "average

precision" is .3408 and .3397 respectively.

A possible explanation for the little difference made

by expanding the queries is that the expansion parameters used were too conservative. To test this hypothesis, additional runs were made using the same set of

synsets but allowing longer chains of links and/or using

greater relative link weights (the a's). Table 2 lists the

additional combinations tested using both the ad hoc

queries versus the documents on disks one and two,

and the routing queries versus the documents on disk

3. As was the case for the routing training runs, the

strategy used for the official TREC-2 runs (all links of

length one, a's = .5) was the most effective expansion

strategy. The more aggressive expansion strategies did

make larger differences in retrieval effectiveness compared to the unexpanded queries, but across the set of

queries the aggregate difference was negative. Hence it

is unlikely that the conservative expansion strategy is the reason for the lack of improvement.

4 Conclusion

The experimental evidence clearly shows this query ex- pansion technique provides little benefit in the TREC

environment. The most likely reason for why this should be so is the completeness of the TREC topic descriptions. Query expansion is a recall-enhancing tech- nique and TREC topic descriptions are already large

compared to queries found in traditional IR collections. Although most of the expanded queries did have some new terms added to them, the most important terms

frequently appeared in both the original term set and

the set of expanded terms. This had an effect on the

relative weight of those terms in the overall similarity

227
Expansion by synonyms only

orig terms a synonyms a

1 .1 1 .3

1 .5

1 .8 Expansion by synonyms plus all descendents

orig terms a synonyms a descendents a

1 .1 .1 1 .3 .1 1 .3 .3 1 .5 .1 1 .5 .3

1 .5 .5

1 .8 .1 1 .8 .3

1 .8 .5 Expansion by synonyms plus parents and all descendents

orig terms a synonyms a descendents a parents a

1 .1 .1 .1 1 .3 .1 .1 1 .3 .3 .3 1 .5 .1 .1 1 .5 .3 .1 - -t'-- .5 .3 .3 1 .5 .5 .1 1 .5 .5 .3 1 .5 .5 .5 1 .8 .1 .1 1 .8 .3 .1 1 .8 .3 .3 1 .8 .5 .1

1 .8 .5 .3

1 .8 .5 .5 Expansion by synonyms plus any directly related synset

orig terms a synonyms o? other a

1 .3 .1 1 .3 .3 1 .5 .1 1 .5 .3 1 .5 .5

1 .3 .5

Table 1: Combinations of expansion strategies and relation weights tested.

228
A unexpanded A expanded

A

unexpanded

expanded

0.2 0.4 0.6

Recall

0.8 1.0

Figure 4: Effectiveness of expanded versus unexpanded ad hoc queries.

229
Expansion by synonyms plus parents and all descendents

orig terms a synonyms a descendents a parents a III .5 .5 .5 1111.5 .5

Expansion by

12

synonyms plus any directly related 11synset

orig terms a synonyms a other a

1 0 0

1 .5 .5 1 1 1

Table 2: Additional combinations of expansion strategies and relation weights tested.

computed for a document, especially when some important terms had no corresponding synset. Topic 112

provides an example of this effect. The topic is con- cerned about the world-wide investment in biotechnology. I added synonym sets for investment and capital to the topic. WordNet does not contain biotechnology,

although it does contains biomedtcaLscience. Thus, I also added the biomedicaLscience synset and a synset

containing gene. The resulting expanded query per- formed significantly worse than the unexpanded version

(33 relevant retrieved in the first 100 versus 52 relevant

retrieved). The problem is that the expanded query

places too much emphasis on money and not enough on biotechnology. Thus these results indicate that simply recognizing which are the important concepts in a query statement is not sufficient to ensure improved re- trieval performance. An expansion procedure must also preserve the relative weights of those concepts.

Another possible explanation is that WordNet is not

suited for this task — it was not designed to be used in this manner and it may not contain the necessary links. Even if this is true, however, it is unlikely that any other

broad-coverage knowledge base would be better suited. The success of relevance feedback and other routing

techniques suggests that the most useful relations are

specific and idiosyncratic.

A second goal of this work was to characterize the

effectiveness of different types of lexical relations when

used to expand a query. Assuming the set of words to be expanded is well chosen, any closely related word —

regardless of the type of relation — may be a good

additional word. Wang et al. reached a similar conclusion [6]. Nevertheless, an added word should be

weighted less than the original word that caused it to be included. Runs in which added words were

equally or more heavily weighted than original words

were consistently less effective than the more conservatively weighted runs. Similarly, runs that added words

that were loosely related to original words (i.e., when

long paths of links were followed) were consistently less effective than runs that used only near relatives.

Acknowledgements

Geoff Towell carefully read a draft of this paper and

suggested changes that improved its presentation and

clarity.

References

[1] Chris Buckley. Implementation of the SMART in- formation retrieval system. Technical Report 85-

686, Computer Science Department, Cornell University, Ithaca, New York, May 1985.

[2] Chris Buckley, Gerard Salton, and James Allan.

Automatic retrieval with locality information us- ing SMART. In D. K. Harman, editor, Proceedings of the First Text REtrieval Conference (TREC1), pages 59-72. NIST Special Publication 500-207,

March 1993.

[3] Edward A. Fox. Extending the Boolean and Vec- tor Space Models of Information Retrieval with P- norm Queries and Multiple Concept Types. PhD

thesis, Cornell University, 1983. University Microfilms, Ann Arbor, MI.

[4] George Miller. Special Issue, WordNet: An on-line

lexical database. International Journal of Lexicography, 3(4), 1990.

[5] Ellen M. Voorhees and Yuan-Wang Hou. Vector ex- pansion in a large collection. In D. K. Harman, editor. Proceedings of the First Text REtrieval Conference (TREC-1), pages 343-351. NIST Special Publication 500-207, March 1993.

230
[6] Yih-Chen Wang, James Vandendorpe, and Martha

Evens. Relational thesauri in information retrieval. Journal of the American Society for Information

Science, 36(l):15-27, January 1985.

231

TREC2 Document Retrieval Experiments using PIRCS

K.L. Kwok & L. Grunfeld

Computer Science Dept., Queens College, CUNY

Flushing, NY 11367. email: kklqc@cunyvm.cuny.edu

Abstract

We performed the full experiments, using our network

implementation of component probabilistic indexing and

retrieval model. Documents were enhanced with a list of

semi-automatically generated two-word phrases, and queries

with automatic Boolean expressions. An item self-learning

procedure was used to initiate network edge weights for

retrieval. Initial results submitted were above median for ad

hoc, and below median for routing. They were not up to expectation because of a bad choice of high-frequency cut- off for terms, and no query expansion for routing. Later experiments showed that our system does return very good

results after correcting the earlier problems and adjusting some parameters. We also re-design our system to handle

virtually any number of large files in an incremental

fashion, and to do retrieval and learning by initiating our network on demand, without first creating a full inverted

file.

1. Introduction

In TRECl our system called PIRCS (acronym for Probabilistic Indexing and Retrieval -Components- System)

took part as Category B participant, handling only the 0.5 GB Wall Street Journal collection because both our

software and hardware were not sufficient for the full set of

text files. In TREC2, we participated in category A. However, during a large portion of the time period we have

to face fairly uncertain and sometimes difficult conditions. Plans to install a dedicated SPARC10 workstation and

associated large memory and disk drives did not materialize

until about three weeks from the deadline. Before this period, the SPARC2 woricstation that we have been using was also shared with other users during the semester. Certain things that we wished to do were not done, and comers were cut to fit programs and data in the existing

system. Much of our time was spent revamping our

software to be more efficient in space and time utilization. Our focus remains as in TRECl, namely, to improve

representations of documents and queries, to test different

learning methods and to combine different retrieval methods

to improve final ranked retrieval output. Section 2

summarizes our retrieval network; Section 3 discusses our improved system design; Section 4 is on item

representation; Sections 5&6 are about our learning and

redieval procedures; Section 7 discusses the results we

submitted and Section 8 contains results of our later experiments. Section 9 follows with the conclusion.

2. A Retrieval Network in PIRCS

Our retrieval process is based on a three layer Q-T-D

(Query-Term-Document) network, details of which are given in [KwPK93,Kwok9x]. Here we give a review.

From Fig.l, DTQ query-focused retrieval means spreading an initial activation of 1 (one) from a document d; towards query q^ and gated by intervening edges w,,; and w^^. The

resultant activation received at q, is Wj/q = 1^ w^*Wy, and

is the retrieval status value (RSV) of d; with respect to q,. When activation initiated at q^ spreads towards dj, we obtain

activation received at dj equals to Wy^ = Wii,*w,„, and is our QTD document-focused RSV for d; . Combining the two additively: Wj = Wj/^ + Wj/j gives our basic retrieval ranking function. Edge weights w,^, w^ represent items (q, or di) acting on terms t^ and reflect usage of terms within

items. Edge weights w^, Wi^ (representing t^ acting on q, or dj) embed Bayesian inference and are initialized based on

a component consideration of probabilistic indexing and

retrieval. These weights can improve via a learning process when relevant documents are known to queries and vice

versa: DTQ query-focused training when we know the set of documents and their components relevant to a query, and

QTD document-focused training when we know the set of

queries and their components relevant to a document.

Query-focused training prepares queries to match new

similar documents better, while document-focused training

helps documents to match new similar queries better. With

learning capability, the net behaves and can be viewed as a

superposition of two 2-layer direct-connect artificial neural networks, one in each direction. If a boolean expression for query q, is known, it can also be represented as a tree and

hung onto the net as shown in Fig.2. Edge weights from

233
the net are used to initialize the tree leaf nodes, from which DTQ direction resulting in a third RSV: S,. The RSVs are

activation spreads to the query root node. Processing at the latter combined for ranking retrieval outputs, nodes implements soft-Boolean evaluation [SaFW83] in the

3. System Design

Our previous software for TRECl has extraneous

processing that produces several intermediate files for other

PreProcss —> Create —> Initiate

Documents Direct files I Network

I /l\

PreProcss —> " 1 Queries I /l\

I Relevance File —>

In addition, we also took the opportunity to re-design our system resulting in several ianovative ^proaches described

in die following subsections:

3.1 Full Inverted File Eliminated

It is useful to view a texd)ase as a document by term

matrix. If one stores the matrix rowwise, we call it a direct

file, in contrast to an inverted file which stores die matrix columnwise. The inverted file is useful to support fast

retrieval while the direct file is useful for feedback learning

and query expansion when given certain documents being

relevant to certain queries. In addition, die raw textfile is

purposes. These consume a lot of disk space for large scale

collections. We spent a substantial amount of rime to revamp our system, resulting in a more streamlined flowchart as follows:

useful for display purposes after a retrieval ranked list is produced K one assumes that each of diese three files are approximately equal in size of N bytes, we need a minimum

of 3N bytes, which is quite substantiaL Removmg the raw

text may not win user support during display unless die

direct file encodes all stopwords. punctuations and

paiagrqih structines of die originaL Most systems delete die direct file and re-produce a subset of it fiom raw text when needed This results in a requirement of 2N bytes.

Wechoose, however, to keep the direct file and produce our network widi respect to the queries dynamically as needed

widioot first producing an inverted file first Our network

actually contains bodi direct and 'inverted' data. It resides

in memory to support learning and retrieval By this

—> Network —> Retrieve —> Evaluate

Learning and Rank

234
approach we achieve several objectives for our system: a) reduce the 'dead' time between a collection being acquired

and its availability for searching, since die full inverted file

is not produced; b) satisfy the 2N bytes of space

requirement; c) support fast feedback learning and query

expansion by having the direct file available. The price we

pay is to have to create the network dynamically, unlike the

full inverted file which is produced once. However, since the full inverted file is too large to reside in memory,

reading parts of it in for retrieval is also time consuming.

3.2 Reduced Network Size

We produce our network in memory according to the queries under attention and the terms used in them. Since

memory is limited, docimients are divided into subcollections (Section 3.3) and queries are linked in five

to ten at a tune. We define the active term set (ATS) of a network as the set of terms used by the current queries and

their feedback documents if any. The latter will provide terms for query expansion. Only edges that connect items

to the active term set are initiated in the network, reducing

network space requirements substantially. Witii die current implementation for 1GB subcoUection and 7 queries, the node and edge files together take about 40 MB. Using

clock time as a measure, producing die network requires

about 40 minutes. Learning is fast, about 2 minutes, and

retrieval and ranking anotiier 8 minutes. We hope tiiat further improvements in design and faster hardware in the future can improve these figures substantially.

3.3 Master and SubcoUection File Structure

We view the TREC experiments as document retrieval from

multiple collections, but reporting retrieval results in one

single ranked list of documents for each topic (query).

Although only four or five collections such as Wall Street Journal, Associated Press, etc. are given in TREC, in reality

it could be many more. We consider diree methods in file design to approach the problem:

a) A centralized approach where all documents from all collections are processed as if fi'om a single document

stream, producing a centralized dictionary containing full usage statistics and a giant direct file. From tiiese, networks can be initialized. The idea is simple, but it has

drawbacks in that eventually the direct file would exceed

file/disk size limitations and software has to be designed to handle data crossing file/disk boundaries. Moreover, it is inherentiy fi'agile to create single files of this size. The

advantage of this approach is tiiat RSVs calculated are

directiy comparable for all documents and a single ranked

list is produced widiout difficulty.

b) An independent collections approach where each source

collection of about 0.5-1.0 GB say. forms a textbase with its own local dictionary and direct file, for network initiation and retrieval. One simply repeats the process for as many

collections as necessary. This is the preferred approach,

and if one has n processors and sufficient disk space, n

separate textbases can be created for learning and retrieval in parallel saving substantial time. The problem is how to combine the retrieval lists from each mto a single ranked

list, since each textbase has its own term usage statistics and

calculates RSVs for ranking within its own environment.

Classical Boolean retrieval and coordinate matching pose no

problem. Some retrieval strategy may produce RSVs that are comparable across collections in theory; but after approximations are taken, it is questionable that this is still true. Similar problem exists for retrieval from distributed databases such as tiie WAIS environment.

c) For TREC2 we settie on a hybrid subcollections

approach, treating each source as a subcoUection widiin a master. We create a master centraUzed dictionary as in a) capturing full usage statistics serving ail the subcollections. and create separate direct files for each subcoUection as in

b). The central dictionary has about 620.000 unique terms

after processing 2 GB fiom Diskl and Disk2, and is relatively small. It captures global term usage statistics, while the individual direct files capture local usage statistics within items. Separate networks are then created for each

subcoUection with edge weights based on the correct global

and local statistics as in a), assuring that retrieval lists contain RSVs that are directiy comparable. This approach

combines the advantages of botii a) and b), and can also function in a paraUel distributed environment.

4. Item Representation

As in TRECl. a number of preprocessing mainly for the purpose of improving the representations of documents and

queries are done as foUows:

4.1 Vocabulary Control

In addition to a manual stopword list of about 630 words

and another 528 manually identified 2-word phrases, we

also process samples from Diskl and Disk2 using aU five source types (WSJ. AP, FR. ZIFF and DOE. of about

1(X)MB each) to produce two-word phrases based on

adjacency within sentence context. Our objective is to remedy losses of recaU and precision due to the removal of

high frequency terms. Our criteria for phrases is that each

word pair must have a frequency of 40, and either one or both components must be high frequency (>= 10000). A

casual scan of tiie resultant Ust generated led us to remove

235
some obvious non-content phrases (such as 'author describ',

paper attempt', 'two way'). The result is 13.787 pairs.

These plus the previously prepared manually list (which

contains mostly of phrases with at least one stopword as well as some phrases identified in the 'topics') are then

treated as if they were single index terms to be identified during document and query processing [BuSA93]. They

have their own global and local usage statistics, and can improve individual collection retrieval effectiveness from a few to 10%.

After documents are processed, we invoke Zipfs law to remove low (=4) and high frequency (=16(X)0) words from

being used for representation. Low frequency terms lead to too many nodes, and high frequency terms lead to too many

edges, both consuming valuable memory space. Unfortunately, our high frequency cut-off of 16000 was a bad choice. In fact, we used high = 12(X)0 for routing

because at that earlier period, we were short of resources. The effect is that our queries become too short and many

useful terms (such as 'platform' in query #80. 'crimin' for criminal in query #87. etc.) are screened out. We discover

that this is a major factor in our disappointing results. Later experiments use a high cut-off of 500(X),

4.2 Subdocuments

As in TRECl we segment documents into subdocument

units to deal with the problems of WSJ documents having

multiple unrelated stories, and long documents in general.

A reaUy long document is FR89119-01 1 1 which has 400748

words. Our criteria is to break documents either on story

boundaries or on the next paragraph boimdary after a run of

360 words for all collections. We have not foimd

convenient story boundary indicators m other collections as

in WSJ (which uses three dashes ' —' on a line). With this scheme, the total number of subdocuments from Disk 1&2

becomes 1.281,233 compared with an original 742,611.

After the TREC2 deadline, we have the resources to investigate the effects of subdocuments on retrieval. Experiments were performed on individual subcollections

WSJl, FR2, FRl and AP2, using segmiaitation sizes of 360,

720 and 1080 words. For WSJl. we further break on story

boundaries only. Results are tabulated in Table 1. It appears that for the abnormally long documents of FR.

breaking mto subdocuments is definitely worthwhile,

achieving improvements of over 20% compared with no

segmentation. However, for the newswire documents of

Asis Break on

" Stories"

1080 720 360

WSJl

Avll

# of docs

FRl

Avll

# of docs

FR2

Avll

# of docs

AP2

Avll

# of docs

0.421

98733

0.289

26207

0.333

20108

0.423

84678

0.432

127151

0.428

134819

0.351

50055

0.372

0.423

85616

0.424 0.418

149611 193881

0.354

64650

0.420

51607

0.404

95867

0.354

108374

0.421

86787

0.414

146354

Table 1: Document Segmentation Avll Precision using 50 Queries Q2

WSJl and AP2. subdocuments have marginal performance

effects: a little better for large chunk sizes, and a little worse for small chimks. It seems that a chunk size of about

720-1000 words would get the benefits of both types. Using

different chunk sizes for different collections would

probably not be worth the effort. We like to point out that other than effectiveness,

considerations such as isolating relevant sections for output

display and for more precise feedback judgment would also make document segmentation worthwhile. In particular, a number of long documents in feedback for query expansion

would easily overload memory space in our network.

4.3 Queries

Topics are preprocessed to remove introductory phrases and

236
non-content terms based on word sequence patterns. We

contuwe to use words from the title, description, narrative and concept sections of the topics to form queries.

Experiments without the narrative section show slight

decrease in performance for oiu: system in contrast to [Gof93]. We also try to produce automatically Boolean

expressions as queries from the description and concepts

sections. This is done by using pimctuations to delineate phrases and ANDing the words within them. Phrases are then ORed. This is a very crude way of getting Boolean

expressions for later soft-Boolean retrieval processing.

5. Learning Procedures

In our network of Fig.l the edge weights determine the

retrieval outcome, w^j and w,., captures the proportion of term t^ in d; or q^. They are fixed and obtained from the manifestation of terms in the respective items, w^ (and w-j)

has a log odds factor, and an Inverse Collection Term

Frequency (ICTF) factor which is regarded as a constant for a term t|j, as follows:

w.k = In [rjd-rj] + ICTF, (1)

Here is the conditional probability that given relevance

to q, that term t, occurs, and needs to be learnt. It is unknown unless one has a sample of relevant documents to

q^. This is not applicable to initial ad hoc retrievals where

a document collection is being processed against a new

query with no known results. Relevance feedback

mformation can remedy this later, but it is not available at the beginning. One way is to ignore the log odds factor in Eqn.1, as done in our TliECl experiments, resulting in ICTF weighting. A better way, which we use for TREC2,

is to include item self-learning to determine r^, and initiate

the term weights. This is shown in Fig.3 and is based on

the following argument. Consider a document d; containing

certain concepts and topics. Imagine this author wishing to inquire the textbase for the same topics as this document

s/he has written; what query would be most suitable? Naturally the author's own words in die document can serve

as the 'query', and there is also known to be one relevant item to this 'query' in the collection, viz. the document

itself. In other words, every item is assumed to be self- relevant. One relevant item is however not sufficient for estimating r^. Our method is to consider each document as constituted of many independent conceptual components

each bemg described by a list of terms. We therefore work

in a component universe rather than in the document

collection. Components can be imits such as sentences ot

phrases, but we have used single terms for simplicity.

Right from the start then, even without any relevance

feedback, we can divide the component universe into two

parts: one set relevant, and the other non-relevant to each

the 'query'. Standard probabilistic retrieval theory now

enables us to define this 'query' optimally - meaning that the defined 'query' will rank its set of relevant components

optimally with respect to the other components when used

for retrieval. The definition of this 'query' (i.e. its terms and weights) becomes the initial indexing representation of

the document, and it is used in our ad hoc QTD retrieval. This is the principle of document self-recovery introduced

in [Kwok90] and implemented as a self-learning process in a network [Kwok89,9x] shown in Fig.4. One can argue that

this relevant set of components from one document is too

small. But tiiat is all the mformation one has at this stage. Previous experiments [Kwok90] show that this kind of

weighting can outperform ICTF weight by a few percent.

Moreover, our network self-learning parameters can be

adjusted to provide a smooth transition from ICTF to full self-leam weights, or any value in between. We invoke the

di as 'query'

A

as 'query'

A

relev d^^ irrelev relev di —ocomponents

o o

o

o o

irrelev

o

o o

o o o

o o

o o

document space component space

Documents are not monolithic, but constituted of components

Fig.3 Item Self-Learn Using Its Own Self-Relevant Components

237
DTQ

same self-learning procedure for a query by adding the query to tiie collection as a 'dociiment' temporarily,

resulting in self-learn initial weights for the indexing

representation of the query. This weight is used in our ad

hoc DTQ retrieval. The bottom line is that this component

consideration enables the probabilistic model to self- bootstrap, allows term frequencies in items to be employed

instead of 'binary', and takes into account of query-focused

and document-focused retrieval in a cooperative fashion.

In the case of routing retrieval, relevant documents are available for training each topic q^. These documents are again considered as constituted of components, and are used

in the network to estimate r^. The estimate should be

better than self-learning because the sample size of relevant components is much larger. Our learning algoritimi updates

the conditional probability r^ (and v^) as follows (Fig.5):

Ar^ = 'nQ*(x, - r^"*") (2)

Here, tIq is a learning rate for training on the query side and is the average activation deposited on term t^ by tiie given relevant set. If a term t, not in the original query is highly activated by the relevant set, it can also be linked to

q, with edge weights given by:

w,. = a * X, = P

* TIq * X, (3)

This implements query expansion as was also done in TRECl.

6. Retrieval Methodology

To satisfy TREC2 requirements, we submitted results as named in the following:

pircsl: routing, no training;

pircs2: routing, with training from Diskl relevants,

Disk2 not used and no query expansion;

pircs3: ad hoc, no soft-Boolean added;

pircs4: ad hoc, with soft-Boolean added

Routing allows training the old Q2 topic set (topics 51-100)

before doing retrieval oa new DiskS documents. DiskS

term usage statistics are not used. Ad hoc retrieval involves

using new Q3 topic set (topics 101-150) to do retrieval on

old documents in Diskl and Disk2. All our queries are automatically constructed. We did not perform feedback

experiments using Q3.

For baseline routing (pircsl) and ad hoc (pircs3) retrievals, we use the item self-learn (SL) edge weights. Routing

pircs2 denotes retrieval based on further learning from

known relevant samples and represent improvements from

pircsl. There can be hundreds of known relevants for each

of the Q2 topic set from documents in Diskl and Disk2 as given from the results of TRECl. One way of employing

them is to do a retrieval (ranking) of Diskl and Disk2

documents, and then make use of the first n (say n=100)

relevant documents, as for feedback learning. However, we I did not have enough resources to create a network and do

retrieval (ranking) oa 2GB of documents at that time and

have to settie on a simplified strategy. First, we decided to use Diskl only (1 GB) for training. Secondly, we believe

238
a retrieval (ranking) of Diskl is still expensive and perhaps

not necessary; rather we just select 'nonbreak' relevants from Diskl for training. 'Nonbreak' means documents that do not get split into multiple subdocuments based on our

criteria given in Section 3. The idea is that the quaUty of

documents for training is important, and short relevants are the choice. They may not be those ranked early during a

retrieval. With these simplifications, a netwcffk is produced

with Diskl, the query-term edges are trained, and then

stored for later routing retrieval using DiskS.

Ad hoc pircs4 denotes retrieval based on combming the baseline pircs3 with a soft-boolean retrieval. The pircs4

ranking formula becomes r*Wi+s*Si (see definitions in Sectiai 2). Our Boolean expressions for queries are prodiK^ automatically as discussed in Section 4.3. and

edge weights are used to initiate the leaf nodes of the Boolean expression tree.

7. Discussion of Submitted Results

From our routing retrieval table in the master appendix of

this volume, it can be seen that pircs2 improves over pircsl

by about 7% based on average non-interpolated precision

(.266 vs .249) and about 3.8% based on relevants retrieved (6135 vs 5913), showing that our simplified method of

using only the Diskl 'nonbreak' training documents still works. We did not do a retrieval and rank. Compared with

the oibsi sites, our restilt is below median both using the average non-interpolated precision for individual queries (18

better, 2 equal and 30 below median), and using the

relevants retrieved at 100 documents (18 better, 8 equal and

24 below median). If we assmne the existence of an

overall 'maxi-system' that produces the best noninterpolated precision values among all sites for all 50

queries, then its average precision over all queries is 0.5054

and 8348 relevants retrieved. Our pircs2 achieves only

.266/.505 = 52.7% of the average precision but 6135/8348 = 73.5% of the relevants retrieved.

From the ad hoc retrieval table in the appendix cf this volume it can be se&i that pircs4. which is pircs3 combined

with automatic soft-Boolean retrieval, improves over pircs3

only by about 1%. Processing time however increases

substantially. Our automatic Boolean expressions are awlely formed; manual Boolean queries may do better. Compared with other sites, our result is above median both

using the average non-interpolated precision for individual

queries (34 better, 2 equal and 14 below median), and using

the relevants retrieved at 100 documents (36 better, 4 equal

and 10 below median). The 'maxi-system' has an average

precision over all queries of 0.4354 and 9021 relevants

retrieved. pircs4 adiieves about 0.298/0.435 = 68.5% of

this best precision value and 7464/9027 = 82.7% of the

relevants retrieved. They are much better than for routing.

It would be most useful and interesting if one can choose

the best reported result for each query before the answers

are known. For these experiments our high frequency term

cut-off is 16000, which is still too low. The next Section

discusses oiu: later results.

8. Further Experimental Results

After the TREC2 Conference, we decided to repeat both

experiments. We realize that our disappomting results are due to several factors: 1) bad high fiequency term cut-off leading to insufficient representation; 2) no query expansion;

3) insufficient training samples; and 4) parameters need

tuning. Except for 4) these are remedied as follows: highfrequency cut-off is set at 5(X)00, learning for routing is done from both diskl and disk2 and only documents that 'break' into six or less subdocuments are used, and query

expansion is also done. The runs are named in Table 2 as:

pircs5: routing, wifli learning but no query expansion;

pircs6: routing, query expansicm level of 20;

pircs7: routing, 'upperbound', no expansim;

pircs8: ad hoc without Boolean queries.

As in TRECl, our query expansion level of 20 actually adds

less than 20 terms because some of the top-ranked terms may already appear in the query. It can be seen that results are substantially better than tiiose in Section 7. In

particular, pircs6 routing witii query expansion have average

precision of 0.355 and die number of relevants retrieved are 7476 out of 10489. These are 12% and 5% respectively

better than pircs5 (0.318, 7098): routing with learning but no query expansion, and achieving 70.3% and 89.6% of the maxi-system values. The same average precision value and

relevants retrieved for ad hoc retrieval pircs8 are 0.344 and

8279 out of 10785, representing 79% and 91.7% of the ad

hoc maxi-system respectively. At 20 docimients retrieved, the precision values for routing and ad hoc are respectively

0.583 and 0.564. This means that averaging over 50

queries, out of the first 20 retrieved over 11 are relevant. Considering the size of these textbases, these are quite good

results. These numbers are user-oriented, and users

naturally hope to see 1(X)% precision. As discussed in TRECl, from a system point of view the precision at n

documents retrieved should not be compared to the

theoretical value of 1.0, but to an q)erational precision

value x/n if the total number of relevants x for a query is less than n. For example, at n=100 documents retrieved 20

routing and 16 ad hoc queries have total relevants x less than 100. The operational maximum precision averaged

over 50 queries for routing is only 0.8, and that for ad hoc

is 0.871. At 100 documents, routing pircs6 value of 0.439

and ad hoc pircs8 value of 0.468 therefOTe achieves 54.9%

239
Revised Routing New Ad hoc

pircsS pircs6 % pircs7 % pircsS

Total number of documents over all 50 queries

Retrieved: 50000 50000 50000 50000

Relevant: 10489 10489 10489 10785

ReLret: 7098 7476 + 5J 7385 + 4.0 8279

Interpolated recall - precision averages:

0.0 .765 .814 + 6.4 .793 + 3.7 .823

0.1 .554 .628 +13.3 .594 + 7.2 .561 0.2 .491 .546 +11.2 .534 + 8.6 .505 0.3 .421 .469 +11.4 .469 +11.4 .456 0.4 .380 .413 + 8,7 .416 + 9.5 .417

0.5 .336 .363 + 8.0 .368 + 9.5 .368 0.6 .270 .310 +14.8 .314 +16.3 .320

0.7 .212 .240 +13.2 .241 +13.7 .246 0.8 .150 .165 +10.0 .171 +14.0 .160 0.9 .079 .099 +25.3 .098 +24.0 .087

1.0 .014 .006 -57.1 .015 + 7.1 .012

Average precision (non-interpolated) over all rel docs

.318 .355 +11.6 .350 +10.1 .344

Precision at5 docs: .600 .660 +10.0 .624 + 4.0 .612 10

" .582 .632 + 8.6 .598 + 2.7 .572 15

" .545 .617 +13.2 .572 + 5.0 .573 20 " .527 .583 +10.6 .563 + 6.8 .564 30 " .507 .553 + 9.1 .534 + 5.3 .540 100 " .402 .439 + 92 .427 + 6.2 .468 200 " .334 .360 + 7.8 .353 + 5.7 .396 500 " .222 .238 + 7.2 .232 + 4.5 .264 1000 " .142 .150 + 5.6 .148 + 4.2 .166 R-precision

Exact .358 .385 + 73 .385 + 7.5 .378

Table 2: Revised Routing and New Ad Hoc Retrieval Results

and 53.7% of the operational maximum. Tlie R-precision Exact calculates the precision value at x retrieved, where x

is the known number of relevants for each query, and can

be compared widi the theoretical value of 1.0.

The 'Upperbound' retrieval pircs7 (suggested by SparckJones) means performing learning from the known Disk3

(not Disk 1«S^) relevant documents before retrieval. In other words, we assume the answer documents are known

for training and represent the best that probabihstic thecM7 can provide using our system This is however not the true upperbound [Spar79] for retrieval from Disk3. because the vocabulary and usage statistics are still those of Disk 1&2.

The vocabulary is retained for comparison with routing

results. pircs7 retrieval achieves average precision of 0.350,

which improves over pircs5 (training from Disk 1&2) by

about 10% in average precision and about 4% in relevants

retrieved. Of course in real life, the answer documents are not known; but it is interesting to note that query expansion

using Disk 1&2 documents can provide similar performance, showing the importance of query expansion.

We later concentrate on routing and discover that additional gains can be achieved by fine tuning of the parameters in our model. For learning: 1) we find that our original

method of using only 'nonbreak' documents in the given set of relevants actually outperforms other document selection

strategies including using aU relevants, 'break six' or

240
New Routing Results

No

Train.

Trained

Exp 0

Trained

Exp 20

% Trained

Exp 40

% Trained

Exp 80

% Trained %

Exp 100

Total number of documents over all 50 queries

Retrieved:

Relevant

Rel ret:

50000

10489

6551

50000

10489

6961 + 6.0

50000

10489

7496 +14.0

50000

10489

7646 +17.0

50000

10489

7695 +17.0

Interpolated Recall - Precision Averages:

50000

10489

7712 +18.0

0.00 0.7200 0.7480 + 4.0 0.8471 +18.0 0.8475 +18.0 0.8646 +20.0 0.8660 +20.0

0.10 0.5124 0.5815 +13.0 0.6645 +30.0 0.6751 +32.0 0.6810 +33.0 0.6801 +33.0

0.20 0.4431 05254 +19.0 0.5981 +35.0 0.6116 +38.0 0.6135 +38.0 0.6115 +38.0

0.30 0.4016 0.4728 +18.0 0.5371 +34.0 0.5413 +35.0 0.5465 +36.0 0.5452 +36.0

0.40 0.3486 0.4402 +26.0 0.4751 +36.0 0.4774 +37.0 0.4829 +39.0 0.4878 +40.0

0.50 0.2970 0.3862 +30.0 0.4167 +40.0 0.4288 +44.0 0.4229 +42.0 0.4214 +42.0

0.60 0.2382 0.3048 +28.0 0.3496 +47.0 0.3681 +55.0 0.3699 +55.0 0.3690 +55.0

0.70 0.1945 0.2430 +25.0 0.2772 +43.0 0.2880 +48.0 0.2815 +45.0 0.2843 +46.0

0.80 0.1284 0.1865 +45.0 0.1911 +49.0 0.1937 +51.0 0.1999 +56.0 0.2005 +56.0

0.90 0.0740 0.0860 +16.0 0.1130 +53.0 0.1144 +55.0 0.1219 +65.0 0.1238 +67.0

1.00 0.0119 0.0187 +57.0 0.0140 +18.0 0.0107 -10.0 0.0114 -4.0 0.0171 +44.0

Average precision (non-interpolated) over all rel docs

02905 0.3517 +21.0 0.3962 +36.0

Precision at 5 docs: 0.5600 0.5760 + 3.0 0.6960 +24.0

10 docs: 0.5440 0.5820 + 7.0 0.6880 +26.0

15 docs: 0.5173 0.5627 + 9.0 0.6573 +27.0

20 docs: 0.4910 0.5510 +12.0 0.6470 +32.0

30 docs: 0.4653 0.5313 +14.0 0.6147 +32.0

100 docs: 03698 0.4396 +19.0 0.4824 +30.0

200 docs: 0.3049 0.3562 +17.0 0.3887 +27.0

500 docs: 0.2038 0.2241 +10.0 0.2452 +20.0

1000 docs: 0.1310 0.1392 +6.0 0.1499 +14.0

0.4050 +39.0 0.4084 +41.0 0.4095 +41.0

0.7160

0.6860

0.6707

0.6540

0.6173

0.4930

0.3945

0.2490

0.1529

+28.0

+26.0

+30.0

+33.0

+33.0

+33.0

+29.0

+22.0

+17.0

0.7320

0.6980

0.6800

0.6630

0.6240

0.4974

0.4002

0.2500

0.1539

+31.0

+28.0

+31.0

+35.0

+34.0

+35.0

+31.0

+23.0

+17.0

0.7280

0.7000

0.6813

0.6610

0.6267

0.5002

0.4004

0.2498

0.1542

+30.0

+29.0

+32.0

+35.0

+35.0

+35.0

+31.0

+23.0

+18.0

R-Precision (precision after R (= num_rel for a query) docs retrieved): Exact: 03346 03942 +18.0 0.4251 +27.0 0.4283 +28.0 0.4281 +28.0 0.4291 +28.0

Table 3: New Routing Results at Several Query Expansion Levels

ranking and selecting the n best Moreover, these 'nonhreak' documents total only 5225. less flian 1/3 of

161 14 relevants used and is therefore very efficient (There

are actually 16400 relevants from Disk 1&2. but during

processing a small percentage was lost). 2) All edges and

their weights on the query side of the networic are defined

by the activations deposited by the relevant documents; this means the original query plays no part in their definition. 3) Negative edge weights are set to small positive weights

of 0.1. For retrieval: 4) after ranking, several subdocxmients

of the same document ID may rank high, and we combine

their largest three RSVs in the ratio of 1:0.2:0.05 as the

single reported RSV fcs the whole document Previously we ignored the tiiird, and the ratio for combining the largest

two was different We choose to stop at two or three subdocuments because noise from long documents may

creep back. Such timing of parameters led to the results in Table 3 for our latest routing results. We use the convention 'Trained Exp K' to denote query expansion level K, with K=0 meaning weight adaptation without adding new

241
terms. The 'No Train.' column shows resvilts without using

any known relevants for training and serve as the basis for comparison. It can be seen using the measure of average

precison over all recall points that training without query expansion improves over no training by 21%, and training

with expansion at the 40 level improves over the basis by

about 39%. This measure, as well as the R-precision, seems to level off from expansion level 40 onwards.

However the number of relevants retrieved improves from

6551 (no training) to 7712 (expansion level 100) in a mcQOtoae fashion. Higher query expansion level appears to improve the high-recall region of the precision-recall curve without materially affecting the low-recall region as observed in [Kwok9x] using the WSJ collection only.

Precision values at the different cutoffs of documents

retrieved seem to level off at the expansion level of 80. At

20 retrieved documents cutoff, we now achieve a precision over 0.65, meaning that more than 13 of the 20 documents

retrieved are relevant on the average. The tuning of

parameters give us over 10% additional improvements

above those obtained in the revised routing results of Table

2. It appears that a query expansion level of 40 achieves a compromise between good effectiveness and good

efficiency for our system. We did not do massive query expansion at high levels of 200 or more. However, the

results are comparable to the best of those reported in the TREC2 conference.

9. Conclusion

We have upgraded our PIRCS system to use dynamic

network creation for learning and retrieval, and to handle

files in a master-subcollection design. The former approach

allows us to eliminate full inverted file creation resulting in 2 x collection size space requirement, reduced 'dead' time

for a collection to be searchable, and provide fast learning.

The latter approach renders our system to be sufficiently flexible to handle a large nimiber of files in a robust

fashion, yet produce a retrieval ranked list as if all documents were in one file. Although our submitted results for TREC2 were not up to expectation because of

insufficient resources at the time of the experiments, the reasons for the behavior of our system were isolated. New

experiments show that PIRCS can provide highly

competitive retrieval effectiveness in both ad hoc and

routing environments.

Acknowledgment

Ms. Lu CH-Ni provided die program for our two-word

phrase detection. We would like to acknowledge the continual support of the Department C!hairman and the Dean

of Mathematics and Natural Science at Queens College

throughout the project. This work is partially supported by

a grant fi-om ARPA and a PSC-CUNY grant #663288.

References

[BuSA93] Buckley. C. Saltoa, G & Allan, J (1993).

Automatic retrieval with locality information using SMART.

In: The First Text REtrieval Conference (TREC- 1). Harman,

D.K. (Ed.). NIST Special PubUcadon 500-207. pp.59-72.

[Crof93] Croft, W.B (1993). The University of

Massachusetts Tipster project In: The First Text REtrieval

Conference (JREC-l). Harman, DiC. (Ed.). NIST Special

Publication 500-207. pp.101-105.

[KwPK93] Kwok. KX, Papadopolous, L & Kwan, Y.Y.

Retrieval experiments with a large collection using PIRCS.

In: The First Text REtrieval Conference (TREC- 1). Harman,

D.K. (Ed.). NIST Special Publication 500-207. pp. 153- 172.

[Kwok90] Kwok, KL (1990). Experiments widi a component thewy of probabihstic information retrieval based on single terms as document components. ACM TOIS

8:363-386.

[Kwok9x] Kwok, K.L (199x). A network approach to

probabilistic information retrieval. Accepted for publication

in ACM TOIS.

[SaFW83] Salton. G; Fox, E.A & Wu, H (1983). Extended

boolean information retrieval. Comm. ACM 26:1022-1036.

[RoSp76] Robertson, S.E & Sparck Jones, K (1976).

Relevance weighting of search terms. J. ASIS. 27:129-146.

[Spar79] Sparck Jones, K (1979). Bcperiments in relevance

weighting of search terms. Mo. Proc. Mgmnt. 15:133-144.

242
Combination of Multiple Searches

Edward A. Fox and Joseph A. Shaw

Department of Computer Science

Virginia Tech, Blacksburg, VA 24061-0106

Abstract

The TREC-2 project at Virginia Tech focused on methods for combining the evidence from multiple retrieval runs to improve retrieval performance over any sin- gle retrieval method. This paper describes one such

method that has been shown to increase performance

by combining the similarity values from five different retrieval runs using both vector space and P-norm ex- tended boolean retrieval methods.

1 Overview

Table 1: SMART weighting schemes used for TREC-2.

SMART

label term_weight —

ann 0.5 + 0.5*

bnn 1 mnn */ max-t f ain 0.5 + „ * /o^f °"

)

2*max-tf ''V coll-freq ' nnn tf

The primary focus of our experiments at Virginia Tech

involved methods of combining the results from vari- ous divergent search schemes and document collections. We performed both routing and ad-hoc retrieval experiments on the provided test collections. The results from

both vector and P-norm type queries were considered in determining the probability of relevance for each document in an individual collection. The results for each

collection were then merged to create a single final set of documents that would be presented to the user.

2 Index Creation

This section outlines the indexing done with the document collection provided by NIST. Each of the indi- vidual collections was indexed separately as document

vector files; limitations in disk space prohibited the use

of inverted files and the creation of a single combined

document vector file. All processing was performed on a DECstation

5000/25 with 40 MB of RAM using the 1985 release of the SMART Information Retrieval System [2], with

enhancements from previous experiments as well as a new modification for our TREC-2 experiments.

The index files were created from the source text via

the following process. First, the source document text provided by NIST was passed through a preparser to convert the SGML-like format to the proper format for

the 1985 version of SMART. The extraneous sections

of the documents were filtered out at this point. The

TEXT sections of the documents, as well as the various

HEADLINE, TITLE, SUMMARY, and ABSTRACT

sections of the collections were indexed; all of the other

sections were ignored. The subsections of the TEXT

fields, where they existed, were considered as part of the

TEXT field, with the subsection delimiters removed.

The resulting filtered text was tokenized, stop words

were deleted using the standard 418 word stop list provided with SMART, and the remaining non-noise

words were included in the term dictionary along with

their occurrence frequencies. Each term in the dictio- nary has a unique identification number. A document

vector file was created during indexing which contains

for each document its unique ID, and a vector of term

IDs and term weights. The initially recorded weights can be changed based on one of several schemes after the indexing is complete. The various SMART weighting schemes referred to within this paper are summarized in Table 1. The dictionary size for each collection was approximately 16 MB, while the document vector

files ranged from 31 MB to 124MB (see Table 2).

243
Table 2: Collection statistics summary. Text, Dictionary and Document Vector sizes in Megabytes.

LJOC* xoiai

i_/Oiieciioii xext V tiC LOl a Docs

AP-1 266 16.0 120,2 84678

DOE-1 190 15.9 97.9 226087

FR-1 258 ICO 15.8 CO o 53.8 26207

WST-I VV OJ L 1 6 2 1 24 8 ZIFF-1 251 15.7 88.4 75180

Dl 1260 N/A 485.1 510887

AP-2 248 15.9 110.4 79923

FR-2 211 15.6 42.7 20108

WSJ-2 255 16.0 105.5 74520

ZIFF-2 188 15.4 63.6 56920

D2 902 N/A 322.2 231471

Dl & D2 2162 N/A 807.3 742358

AP-3 250 15.9 111.2 78325

PATN-3 254 15.6 31.3 6711

SJM-3 319 16.1 114.4 90257

ZIFF-3 362 16.0 109.8 161021

D3 1185 N/A 366.7 336314

Total 3347 N/A 1174.0 1078672

3 Retrieval

3.1 Queries

All of the queries were created from the topic descriptions provided by NIST. Two types of queries were used,

P-norm extended boolean queries and natural language

vector queries. A single set of P-norm queries was cre- ated, but was interpreted multiple times with different operator weights (P-values). Two different sets of vec- tor queries were created from the topics, one containing information from fewer sections of a topic description. The Title, Description and Concepts sections of

the topic descriptions were used in the creation of all three sets of queries, the Definitions section was used

also in both sets of vector queries, while the P-norm

query set and one of the vector query sets also con- tained information from the Narrative section of the

topic descriptions. The vector query set that included

the Narrative section of the topic is referred to as the

long vector query set, for obvious reasons, while the

other is referred to as the short vector query set. The P-norm queries were written as complex boolean

expressions using AND and OR operators. Phrases were simulated using AND operators since the queries were intended only for soft-boolean evaluation. The

query terms were not specifically weighted; uniform operator weights (P-values) of 1.0, 1.5 and 2.0 were used

Table 4: Summary of the five individual runs.

Title Query Type Similarity Measure

SV Short vector Cosine similarity

LV Long vector Cosine similarity

Pnl.O P-norm P-norm, P = 1.0 Pnl.5 P-norm P-norm, P = 1.5 Pn2.0 P-norm P-norm, P = 2.5

on different evaluations of the query set.

3.2 Individual Retrieval Runs

The first step in our TREC-2 experiments involved de- termination of what weighting schemes would be most

effective for P-norm queries. Our TREC-1 experiments

with P-norm queries had obtained mixed results, per- forming poorly based on binary document term weights

in our Phase I experiments and performing well for a Pvalue of 1.0 and very poor with larger P-values in our Phase II experiments using a tf-idf weighting scheme

[4] . We performed several P-norm retrieval runs on the

two AP and two WSJ training collections with topics

51 to 100 to determine the most effective term weighting scheme for P-norm queries with large test collec- tions. The results from these experiments are shown

in Table 3 using the standard TREC-2 average noninterpolated precision and the exact R-precision measures. The most effective weighting scheme turned out

to be the SMART ann weighting scheme, which con- firmed the result obtained originally by Fox for the

much smaller classical document collections [3]. The two sets of vector queries were evaluated us- ing the standard cosine correlation similarity method

as implemented by SMART. The same SMART ann

weighting scheme used for the P-norm queries was used on the vector queries for several reasons. First, a

weighting scheme that did not use any collection statistics was needed for the routing experiments. Second,

the methods used in combining runs described in the next section required a similar range of possible similarity values produced by each run. Finally, the necessity of merging results from each collection into a single

set of results was simplified since the resulting similarity values were not based on collection statistics which

would have differed for each collection. The P-norm

queries were evaluated using three different P-values,

again using the SMART ann weighting scheme based on specific P-norm experiments described below. The

five individual runs are summarized in Table 4. The five individual runs were performed and evaluated for each of the nine training collections on topics

51 to 100. The results for these experiments are given in

244
Table 3: Average Precision and Exact R-Precision for P-norm experiments on weighting with the AP and WSJ

collections (Ad-hoc Topics 51-100).

Average Precision R- Precision

Coll. P-value ann bnn mnn ann bnn mnn

1.0 n 941Q fl 141Q nu.zuuu 9fif^n AP-1 1.5 0.3122 0.2581 0.1444 0.2976 0.2732 0.1757

2.0 n 9^in D 14^7 'J . Z 1 to n 1707

1.0 u.ouu^ nU.ZD9fi791 Z n 1 S9R U.OIDO

1 5 0.3332 0.2999 0.1831 0.3412 0.3118 0.2161

2.0 0.3300 0.2922 0.1847 0.3339 0.3057 0.2284

1.0 0.2941 0.2485 0.1742 0.3221 0.2830 0.2181

WSJ-1 1.5 0.3199 0.2753 0.1774 0.3443 0.2994 0.2225

2.0 0.3217 0.2752 0.1776 0.3470 0.3013 0.2277

1.0 0.2206 0.1881 0.1356 0.2367 0.2094 0.1722

WSJ-2 1.5 0.2327 0.2013 0.1174 0.2511 0.2234 0.1549

2.0 0.2325 0.1970 0.1098 0.2442 0.2158 0.1445

Table 5. In general, the P-norm queries performed better than the vector queries. The most effective P-value

however differed between the collections: The AP runs performed better with a P-value of 1.5, while a P-value

of 2.0 performed better for the WSJ collections.

3.3 Combination Retrieval Runs

Our experiments in TREC-1 involved combining the

results from several different retrieval runs for a given

collection either simply taking the top N documents re- trieved for each run, or modifying the value of N for each run, based on the eleven point average precision

for that run. We felt these efforts suffered from considering only the rank of a retrieved document and not

the actual similarity value itself. In TREC-2, our ex- periments concentrated on methods of combining runs based on the similarity values of a document to each

query for each of the runs. Additionally, combining the

similarities at retrieval time had the advantage of extra evidence over combining separate results files since the

similarity of every document for each run was available

instead of just the similarities for the top 1000 documents for each run. While our results for four of the

training collections indicated that the P-norm queries

performed better than the vector queries, this result was likely specific to the actual queries involved and

not necessarily true in general. This lead to a decision to weight each of the separate runs equally and not fa- vor any individual run or method. In general, it may

be desirable or necessary to weight a single run more,

or less, depending on its overall performance; this could

be especially useful in a routing situation. For any given information retrieval ranking metohd,

there are two primary types of errors that can occur:

Table 6: Formulas for combining similarity values. Name Combined Similarity — CombMAX MAX(Individual Similarities)

CombMIN MI N{Individual Similarities)

CombSUM SUM{Individual Similarities)

CombANZ SUM{Individual Similarities)

Number of Nonzero Similarities CombMNZ SUM{Individual Similarities)*

Number of Nonzero Similarities

CombMED MED{Individual Similarities)

assigning a relatively high rank to a non-relevant document, and assigning a relatively low rank to a relevant

document. It has been shown that different retrieval paradigms will perform differently on the same set of

data, often will little overlap in the set of retrieved doc- uments. [5] For instance, when one retrieval method

assigns a high rank to a non-relevant document, a differ- ent retrieval method is likely to assign that document a much lower rank. Similarly, when one retrieval method

fails to assign a high rank to a relevant document, a

different retrieval method is likely to assign that document a high rank. This characteristic of information

retrieval methods indicates that some method for considering both retrieval methods together should help to decease the probability of this happening; of course, it

is also possible for both methods to highly rank a nonrelevant document or to poorly rank a relevant docu- ment.

Six methods of combining the similarity values were

tested in our TREC-2 experiments, as summarized in

245
Table 5: Average Precision and Exact R-Precision for the five individual runs (Ad-hoc Topics 51-100).

Average non-interpolated Precision

Run

Disk 1 Dis k 2 Both

AP DOE FR WSJ ZF AP FR WSJ ZF Disks

SV 0.2387 0.0605 0.0222 0.2203 0.1026 0.2543 0.0330 0.1503 0.0770 0.1418

LV 0.2435 0.0586 0.0302 0.2414 0.0864 0.2664 0.0324 0.1633 0.0753 0.1555

Pnl.O 0.2605 0.0658 0.0611 0.2941 0.1110 0.3004 0.0879 0.2206 0.1003 0.1988

Pnl.5 0.2939 0.0771 0.0639 0.3199 0.1278 0.3332 0.0878 0.2327 0.1065 0.2242

Pn2.0 0.2849 0.0847 0.0706 0.3217 0.1278 0.3300 0.0865 0.2325 0.1136 0.2250

CombSUM 0.3493 0.1001 0.0741 0.3605 0.1475 0.3748 0.0842 0.2752 0.1273 0.2620

Chg/Max 18.84% 18.18% 4.95% 12.06% 15.41% 12.48% -4.20% 18.26% 12.05% 16.44%

Exact R-Precision

Run

Disk 1 Disk 2 Both

AP DOE FR WSJ ZF AP FR WSJ ZF Disks

SV 0.2624 0.0564 0.0183 0.2616 0.1180 0.2649 0.0202 0.1744 0.0922 0.2169

LV 0.2672 0.0493 0.0274 0.2800 0.0802 0.2704 0.0176 0.1860 0.0843 0.2311

Pnl.O 0.2688 0.0661 0.0533 0.3221 0.1123 0.3165 0.0971 0.2367 0.0969 0.2708

Pnl.5 0.2976 0.0762 0.0572 0.3443 0.1218 0.3412 0.1016 0.2511 0.1068 0.2962

Pn2.0 0.2968 0.0765 0.0654 0.3470 0.1254 0.3339 0.0820 0.2442 0.1158 0.3008

CombSUM 0.3590 0.0950 0.0619 0.3767 0.1357 0.3732 0.0887 0.2851 0.1216 0.3292

Chg/Max 20.63% 24.18% -5.35% 8.55% 8.21% 9.37% -12.69% 13.54% 5.00% 9.44%

Table 6. The rational behind the CombMIN combination method was to minimize the probability that a

non-relevant document would be highly ranked, while

the purpose of the CombMAX combination method was

to minimize the number of relevant documents being

poorly ranked. There is an inherent flaw with both of

these methods; namely, they are specialized to handle

specific problems without regard to their eff'ect on the

other retrieved documents: for example, the CombMIN

combination method will promote the type of error that

the CombMAX method is designed to minimize, and

vice versa. The CombMED combination method is a

simplistic approach to handling this, using the median

similarity value to avoid both scenarios. What is clearly

needed is some method of considering the documents'

relative ranks, or similarity values, instead of simply

attempting to select a single similarity value from a set of runs. To this end, we tried three other methods of

combining retrieval methods. CombSUM, the summation of the set of similarity values, or, equivalently, the

numerical mean of the set of the set of similarity val- ues; CombANZ, the average of the non-zero similarity

values, that ignores the effects of a single given run

or query failing to retrieve a relevant document; and

CombMNZ to provide higher weights to documents re- trieved by multiple retrieval methods. Cle^arly*, there are more possibilities to consider; the advantages of those

chosen are simplicity, in terms of both execution effi- ciency and implementation, and generality, in terms of not being specific to a given method or retrieval run. These six methods were evaluated against the AP and

WSJ test collections for topics 51 through 100, combining the similarity values of each of the five individual runs specified above. The results are shown in Table 7 below the results of each of the corresponding individual runs from Table 5. Note that while the CombMAX

runs performed well compared with most of the indi- vidual runs, they did not do as well as the best of the

individual runs in most cases. The CombMIN runs per- formed similarly for the AP collection, but performed

worse than every individual run for the WSJ collection. The CombANZ runs and the CombMNZ runs both

performed better than the best of the individual runs, with the CombMNZ runs performing only slightly better than the combANZ runs for three of the four collec- tions, and performing basically the same for the fourth. The primary reason for the similar performance of the

two runs is that the two methods produce the same

ranked sequence of for all the documents retrieved by

all five individual runs. Thus, the

The CombSUM retrieval run was performed for each

of the nine collections on the two training CD-ROMs.

The results are shown in Table 5. Breaking this analysis down to a per topic basis in Table 11, it can be

246
seen that the CombSUM method performs significantly

better than the best single individual run, Pn2.0; a twotailed paired i test on the CombSUM and Pn2.0 average

precisions results in a p value of 3.1e-05, which indi- cates these results are conclusive. However, comparing

the CombSUM results with the best individual runs for each query basis, results in a p value of approximately

about 0.16, indicating that there is a 16 percent chance

that the CombSUM method is no better than the best

individual run, Pn2.0, for any specific query. Performing the same calculation on the R-Precision results in similar significance findings.

While combining all five runs produced an overall improvement in retrieval effectiveness over each of the

runs, the same does not always hold true when combining only two or three runs. Each of the ten combinations of two CombSUM runs was performed for both

of the AP test collections, as well as a run combining

all three of the P-norm runs. The results of these are given in Table 8. Most of the combinations of two runs performed worse than the better of the two runs while

performing better than the poorer of the two runs. One

notable exception to this is the combination of the two

vector runs, which performed noticeably poorer than

either of the two runs.

3.4 Collection Merging

The retrieval results for each of the collections were combined by simply merging the results based solely on

the combined similarity values. Since the retrieval runs were based on term weights without collection statistics such as inverse document frequency, the similarity

values were directly comparable across collections. The

results of merging the CombSUM results by summed

similarity value for both disks, is shown in the last col- umn of Table 5.

4 TREC-2 Results

The procedure described above was used for both our

official TREC-2 routing and ad-hoc results. The exact

queries for ad-hoc topics 51 to 100 used for testing our above method were used for the routing queries against

the new collections on disk 3. The results obtained

from performing the CombSUM retrieval runs for each

of the four collections as well as the merged results are shown in Table 9. The two CombSUM entries in the

last column of table are the official TREC-2 results. Since we concentrated on the ad-hoc evaluations, these

routing results are included primarily for the benefit

of other groups, for purposes of comparison. The adhoc queries for topics 101 to 150 were evaluated in the same manner, and are reported in Table 10. Again,

the official results are the two CombSUM entries in last column of the table. As can be seen from Table 12, the CombSUM method

performs quite poorly for certain topics while performing very well for others, compared to the best single run's results that that topic. Comparing the CombSUM results to the single best individual run (Pn2.0)

shows an improvement for 46 out of the 50 topics, which

shows that the CombSUM run performs much better

than any single individual run. Performing a two-tailed

paired i test on the Pn2.0 and CombSUM precisions

results in a p value of about l.le-11, which indicates

these results are very conclusive. However, comparing

the CombSUM results with the best individual runs on a per query basis results in a p value of about 0.2,

indicating that there is a 20 percent chance that the

CombSUM method is no better than the best individ- ual run for each specific query. Again, performing the same calculation on the R-Precision results in similar

values.

4.1 The CEO Model

The Combination of Expert Opinion (CEO)

model [6, 7] of Thompson can be used to treat the dif- ferent retrieval methods as experts, and allows combining their weighting probability distributions to improve

performance. This could be used in a variety of ways

to combine results from a variety of runs and indexing

schemes (that could include stemming and/or morphological analysis). For TREC-2, the CEO experiments

completed consisted of combining seven individual runs, the three P-norm extended boolean retrieval run types

described above, and retrieval runs based on the long

vector queries, using both cosine correlation and inner

product similarity measures for SMART system term

weighting schemes of nnn and atn. Further discussion

of this process and the results are described elsewhere

in these proceedings.

4.2 Evaluation

Improvements in retrieval effectiveness from combining

the evidence from multiple sources of evidence has been

performed before in various incarnations, most recently

by Belkin ei al. [1] who evaluated the progressive effect of considering multiple soft boolean representations to improve on a base INQUERY natural language retrieval run. In their experiments, the base INQUERY natural

language run performed better than any of the boolean

representations, and they report that combining the re- sults from the natural language representation and the

combined boolean representations with equal weights

performed worse than the best single run. Not until weighting the natural language run four times more

247
Table 7: Comparison of combination runs and the five individual runs (Ad-hoc Topics 51-100).

Average Precision R-Precision

Run AP-1 W IsJ-z A ID 1 Atr-L WsJ-l Ar-Z

sv 0.2387 0.2203 0.2543 0.1503 0.2624 0.2616 0.2649 0.1744

LV 0.2435 0.2414 0.2664 0.1633 0.2672 0.2800 0.2704 0.1860

Pnl.O 0.2810 0.2941 0.3004 0.2206 0.2688 0.3221 0.3165 0.2367

Pnl.5 0.3122 0.3199 0.3332 0.2327 0.2976 0.3443 0.3412 0.2511

Pn2.0 0.3027 0.3217 0.3300 0.2325 0.2968 0.3470 0.3339 0.2442

CombMAX 0.2856 0.3205 0.3337 0.2343 0.3013 0.3484 0.3431 0.2449

CombMIN 0.2863 0.1924 0.3047 0.1308 0.3036 0.2214 0.2980 0.1395

CombSUM 0.3493 0.3605 0.3748 0.2752 0.3590 0.3767 0.3732 0.2851

CombANZ 0.3493 0.3367 0.3748 0.2465 0.3590 0.3517 0.3732 0.2590

CombMNZ 0.3059 0.3368 0.3516 0.2467 0.3175 0.3517 0.3578 0.2590

CombMED 0.2943 0.3204 0.3335 0.2328 0.2977 0.3444 0.3414 0.2518

than the combined boolean schemes did they experi- ence improved retrieval performance when combining

different query methods. This differs from our results

in several ways. Most importantly, the stage at which we combine the different methods differed: Belkin et al. combined the query representations before performing

the actual retrieval, while we combined the similarity

values produced from retrieval on each method individually. The difference between the two methodologies

can best be demonstrated using the standard vector space model: Belkin et al. combined by summing the vector representations of each query, while our method

is analogous to summing the cosines of the angles be- tween each vector and a document. It is easily shown

that the cosine of the angle between a document vec- tor and a combined query vector, that is the sum of

two query vectors as in the Belkin et al. approach, is not equal to the sum of the cosines between a document vector and the two separate query vectors. Other

differences between the two methodologies include the

fact that our P-norm queries performed better on av- erage than our natural language vector queries, with

exceptions on a per query basis. We used only one P- norm query and modified the operator weights while

Belkin et al. used five different boolean queries. Finally, combining with five runs with equal weights ac- tually improved performance over each individual run. However, one common trend emerges from both exper- iments: the more query representations considered, the

better the results.

4.3 Future Exploration

Planned future work includes studying the following:

• Individually weighting various methods' similarity

values when performing combination runs.

• Normalization methods to allow combination of runs made with different weighting schemes.

• Extending the analysis to all combinations of three

and four retrieval runs.

• Considering more/different query types.

5 Acknowledgements

This research was supported in part by DARPA and by

PRC Inc. We also thank Russell Modlin, M. Prabhakar

Koushik and Durgesh Rao for their collaboration during

TREC-1.

References

[1] Belkin, N.J., Cool, C, Croft, W.B., Callan, J.P. (1993, June). The Effect of Multiple Query Representations on Information Retrieval Performance.

Proc. 16th Int'l Conf. on R&D m IR (SIGIR '93), Pittsburgh, 339-346.

[2] Buckley, C. (1985, May) Implementation of the

SMART information retrieval system. Technical

Report 85-686, Cornell University, Department of

Computer Science.

[3] Fox, E.A. (1983, August). Extending the Boolean

and Vector Space Models of Information Retrieval

with P-Norm Queries and Multiple Concept Types.

Cornell University Department of Computer Science

dissertation.

[4] Fox, E.A., Koushik, M.P., Shaw, J., Modlin, R., Rao, D. (1993). Combining Evidence from Multiple

Searches. In TJie First Text REtrieval Conference

248
Table 8: Average Precision and Exact R-Precision for CombSUM runs combining two or three individual runs (Ad-hoc Topics 51-100).

Average Precision R-Precision

Run AP-1 WSJ-1 AP-2 WSJ-2 AP-1 WSJ-1 AP-2 WSJ-2

SV 0.2387 0.2203 0.2543 0.1503 0.2624 0.2616 0.2649 0.1744

LV 0.2435 0.2414 0.2664 0.1633 0.2672 0.2800 0.2704 0.1860

Pnl.O 0.2810 0.2941 0.3004 0.2206 0.2688 0.3221 0.3165 0.2367

Pnl.5 0.3122 0.3199 0.3332 0.2327 0.2976 0.3443 0.3412 0.2511

Pn2.0 0.3027 0.3217 0.3300 0.2325 0.2968 0.3470 0.3339 0.2442

SV and LV 0.1457 0.1657 0.1611 0.1100 0.1492 0.1887 0.1524 0.1124

SV and Pnl.O 0.2774 0.3111 0.3257 0.2362 0.2874 0.3332 0.3360 0.2554

SV and Pnl.5 0.3117 0.3389 0.3614 0.2463 0.3198 0.3575 0.3675 0.2637

SV and Pn2.0 0.3012 0.3395 0.3584 0.2467 0.3153 0.3580 0.3636 0.2551

LV and Pnl.O 0.2744 0.3136 0.3197 0.2353 0.2867 0.3357 0.3269 0.2518

LV and Pnl.5 0.3057 0.3413 0.3536 0.2442 0.3181 0.3596 0.3624 0.2568

LV and Pn2.0 0.2950 0.3408 0.3518 0.2458 0.3141 0.3608 0.3596 0.2615

Pnl.O and Pnl.5 0.2817 0.3109 0 3243 0.2324 0.2898 0.3412 0.3395 0.2476

Pnl.O and Pn2.0 0.2935 0.3191 0.3330 0.2367 0.2944 0.3426 0.3397 0.2507

Pnl.5 and Pn2.0 0.2928 0.3233 0.3336 0.2328 0.2935 0.3478 0.3351 0.2489

Pnl.O, Pnl.5 and Pn2.0 0.2943 0.3192 0.3345 0.2339 0.2953 0.3421 0.3386 0.2497

(TREC-1), D.K. Harmon (Ed.), National Institute of Standards and Technology Special Publication

500-207, Gaithersburg, MD.

[5] Katzer, J., McGill, M.J., Tessier, J. A., Frakes, W.,

Dasgupta, P. (1982). A Study of the Overlap among

Document Representations. Information Technology: Reseach and Development, l(2):261-274.

[6] Thompson, P. (1990) A Combination of Expert

Opinion approach to probabilistic information re- trieval. Part 1: The conceptual model. Information

Processing & Management, 26(3):371-382, 1990.

[7] Thompson, P. (1990). A Combination of Expert

Opinion approach to probabilistic information re- trieval, Part 2: Mathematical treatment of CEO

Model 3. Information Processing & Management,

26(3):383-394.

249
Table 9: Average Precision and Exact R-Precision for the five individual runs compared with the combined CombSUM

runs (Routing Topics 51-100).

Average non- interpolated Precision

Run AP-3 PATN-3 SJM-3 ZF-3 Disk 3

sv 0.1347 0.0189 0.1139 0.0593 0.0589

LV 0.1189 0.0156 0.1056 0.0587 0.0494

Pnl.O 0.2519 0.0257 0.2128 0.1141 0.2039

Pnl.5 0.2869 0.0239 0.2411 0.1189 0.2279

Pn2.0 0.2852 0.0221 0.2390 0.1303 0.2225

CombSUM 0.3196 0.0260 0.2696 0.1304 0.2681

Chg/Max 11.4% 1.2% 11.8% 0.07% 17.6%

Exact R- Precision

Run AP-3 PATN-3 SJM-3 ZF-3 Disk 3

SV 0.1703 0.0171 0.1337 0.0595 0.0595

LV 0.1444 0.0156 0.1098 0.0547 0.1002

Pnl.O 0.2790 0.0325 0.2185 0.1224 0.2594

Pnl.5 0.3082 0.0322 0.2579 0.1248 0.2786

Pn2.0 0.3062 0.0310 0.2531 0.1462 0.2809

CombSUM 0.3319 0.0319 0.2900 0.1260 0.3143

Chg/Max 7.7% -1.8% 12.4% -13.8% 11.9%

Table 10: Average Precision and Exact R-Precision for the five individual runs (Ad-hoc Topics 101-150).

Average non-interpolated Precision

Run

Disk 1 Disk 2 Both

AP DOE FR WSJ ZF AP FR WSJ ZF Disks

SV 0.3237 0.0949 0.0630 0.2740 0.0936 0.3068 0.0650 0.2259 0.1166 0.2035

LV 0.3326 0.0697 0.1018 0.2848 0.0997 0.2981 0.0602 0.2483 0.1045 0.2159

Pnl.O 0.3340 0.0831 0.1777 0.3153 0.1292 0.3133 0.1927 0.2838 0.1722 0.2205

Pnl.5 0.3682 0.0814 0.1874 0.3332 0.1430 0.3438 0.1982 0.2941 0.1964 0.2543

Pn2.0 0.3647 0.0750 0.1761 0.3290 0.1307 0.3419 0.1995 0.2828 0.2018 0.2573

CombSUM 0.4153 0.1038 0.2133 0.3778 0.1657 0.3959 0.2000 0.3561 0.2200 0.3206

Chg/Max 12.8% 9.4% 13.8% 13.4% 15.9% 15.1% 0.2% 21.0% 9% 24.6%

Exact R-Precision

Run

Disk 1 Disk 2 Both

AP DOE FR WSJ ZF AP FR WSJ ZF Disks

SV 0.3351 0.0947 0.0567 0.2881 0.1086 0.3052 0.0718 0.2502 0.1130 0.2649

LV 0.3385 0.0714 0.1006 0.3022 0.1055 0.3019 0.0540 0,2712 0.1036 0.2661

Pnl.O 0.3495 0.0989 0.1434 0.3322 0.1190 0.3139 0.1653 0.2934 0.1577 0.2867

Pnl.5 0.3721 0.0899 0.1582 0.3440 0.1277 0.3465 0.1716 0.3044 0.1796 0.3203

Pn2.0 0.3735 0.0925 0.1579 0.3389 0.1154 0.3401 0.1640 0.2953 0.1979 0.3233

CombSUM 0.4137 0.1156 0.1938 0.3757 0.1389 0.3712 0.1741 0.3385 0.1909 0.3711

Chg/Max 10.8% 16.9% 22.5% 9.2% 8.8% 7.1% 1.4% 11.2% -3.5% 14.8%

250
Table 11: Per-Topic comparison of Average Precisions (Ad-hoc Topics 51-100).

Topic » V

TIT" rnl.U Jrnl.o CombhU JVl Ung/ Max L/iig/ 1 nz.U

74 273 0.0000 0.0099 0.0001 0.0002 0.0002 0.0002 -97.98% 0.00%

90 206 0.1248 0.1134 0.0082 0.0036 0.0015 0.0089 -92.87% 493.33%

77 138 0.1100 0.2241 0.0093 0.0196 0.0246 0.0411 -81.66% 67.07%

91 40 0.1648 0.1039 0.0012 0.0038 0.0068 0.0308 -81.31% 352.94%

72 119 0.1099 0.1173 0.0076 0.0116 0.0138 0.0323 -72.46% 134.06%

73 183 0.0231 0.0124 0.0017 0.0052 0.0120 0.0098 -57.58% -18.33%

94 310 0.0176 0.0185 0.0191 0.1094 0.2100 0.1043 -50.33% -50.33%

85 894 0.1587 0.1779 0.0239 0.0433 0.0518 0.1018 -42.78% 96.53%

67 92 0.0011 0.0012 0.0435 0.0487 0.0502 0.0290 -42.23% -42.23%

55 810 0.2362 0.4535 0.1153 0.1266 0.1256 0.2874 -36.63% 128.82%

64 375 0.1493 0.1641 0.0740 0.0673 0.0706 0.1097 -33.15% 55.38%

57 461 0.0282 0.0605 0.5464 0.4518 0.3447 0.3786 -30.71% 9.83%

80 374 0.0218 0.0266 0.1007 0.1125 0.1025 0.0899 -20.09% -12.29%

82 602 0.2544 0.3139 0.2603 0.1746 0.1278 0.2521 -19.69% 97.26%

97 352 0.0513 0.0513 0.1133 0.1376 0.1405 0.1190 -15.30% -15.30%

76 294 0.1481 0.1677 0.1221 0.0966 0.0755 0.1440 -14.13% 90.73%

89 174 0.0365 0.0762 0.0481 0.1159 0.1515 0.1306 -13.80% -13.80%

98 666 0.0822 0.0677 0.0421 0.0579 0.0703 0.0711 -13.50% 1.14%

84 396 0.1659 0.0985 0.0787 0.1251 0.1559 0.1471 -11.33% -5.64%

99 288 0.4834 0.4796 0.3529 0.3551 0.3224 0.4480 -7.32% 38.96%

58 159 0.0971 0.0620 0.3459 0.4198 0.4072 0.3937 -6.22% -3.32%

92 88 0.0253 0.0405 0.0130 0.0154 0.0176 0.0381 -5.93% 116.48%

71 380 0.0595 0.0613 0.1396 0.1978 0.2210 0.2156 -2.44% -2.44%

88 165 0.1813 0.2341 0.3629 0.3635 0.3542 0.3550 -2.34% 0.23%

59 579 0.2368 0.2272 0.1546 0.3827 0.4370 0.4282 -2.01% -2.01%

93 171 0.0344 0.0385 0.5233 0.4831 0.3779 0.5141 -1.76% 36.04%

95 263 0.0149 0.0228 0.1450 0.2072 0.2392 0.2375 -0.71% -0.71%

52 535 0.4918 0.4462 0.4896 0.6623 0.6882 0.6864 -0.26% -0.26%

78 162 0.2202 0.2632 0.7696 0.7484 0.7250 0.7705 0.12% 6.28%

61 206 0.4106 0.5130 0.3646 0.3786 0.3621 0.5167 0.72% 42.70%

51 138 0.2799 0.3957 0.4751 0.5282 0.5428 0.5476 0.88% 0.88%

70 55 0.1156 0.1198 0.7700 0.7919 0.7982 0.8080 1.23% 1.23%

54 171 0.3063 0.3045 0.3950 0.3207 0.2673 0.4048 2.48% 51.44%

62 298 0.1674 0.1907 0.1519 0.2087 0.2121 0.2198 3.63% 3.63%

81 62 0.1759 0.1410 0.2287 0.2370 0.2321 0.2467 4.09% 6.29%

68 195 0.0960 0.0920 0.1040 0.2098 0.2540 0.2651 4.37% 4.37%

69 52 0.0956 0.1629 0.5382 0.5873 0.5833 0.6227 6.03% 6.75%

53 571 0.1821 0.1874 0.1543 0.2928 0.3241 0.3461 6.79% 6.79%

83 633 0.2673 0.2931 0.1753 0.2412 0.2666 0.3317 13.17% 24.42%

56 878 0.3011 0.3277 0.3391 0.3089 0.2691 0.3955 16.63% 46.97%

100 317 0.1904 0.1972 0.1423 0.1815 0.2094 0.2516 20.15% 20.15%

65 386 0.0100 0.0078 0.1111 0.1190 0.1236 0.1529 23.71% 23.71%

86 213 0.5146 0.4242 0.5216 0.5234 0.4891 0.6624 26.56% 35.43%

60 60 0.0547 0.0887 0.0866 0.0960 0.0992 0.1259 26.92% 26.92%

79 232 0.1376 O.lzzO 0.2057 0.2784 A O'^ 1 A 0.2719 A O A A 0.3690 32.54% 35.71%)

7^1 o ouo nU.UUiO nni fi fiU.UUo nn^7( u.uioi U .UOO'4: O

•^7

1 . 07% U 1 /o 40 9n%

63 208 0.0032 0.0028 0.0631 0.0597 0.0688 0.0972 41.28% 41.28%

66 197 0.0001 0.0000 0.0320 0.0388 0.0478 0.0695 45.40% 45.40%

87 188 0.0436 0.0559 0.0410 0.0914 0.1256 0.1867 48.65% 48.65%

96 693 0.0095 0.0084 0.0833 0.1177 0.1302 0.2356 80.95% 80.95%

Avg 15667 0.1418 0.1555 0.1988 0.2242 0.225 0.262 16.44% 16.44%

251
Table 12: Per-Topic comparison of Average Precisions (Ad-hoc Topics 101-150).

Topic #Rel SV LV Pnl.O Pnl.5 Pn2.0 CombSUM Chg/Max Chg/Pn2.0

103 94 0.2400 0.2379 0.0130 0.0225 0.0309 0.0532 -77.83% 72.17%

122 114 0.1467 0.1059 0.0547 0.0490 0.0544 0.0898 -38.79% 65.07%

101 57 0.2232 0.1932 0.0900 0.1088 0.1175 0.1482 -33.60% 26.13%

140 25 0.0150 0.0520 0.0111 0.0207 0.0315 0.0356 -31.54% 13.02%

135 400 0.4072 0.4449 0.1103 0.1121 0.0835 0.3122 -29.83% 273.89%

104 75 0.1867 0.2214 0.0715 0.0642 0.0828 0.1671 -24.53% 101.81%

121 55 0.0017 0.0028 0.0283 0.0542 0.0628 0.0475 -24.36% -24.36%

127 223 0.2411 0.2476 0.0543 0.0942 0.1028 0.1911 -22.82% 85.89%

143 397 0.4069 0.3978 0.1628 0.1968 0.2022 0.3257 -19.96% 61.08%

124 173 0.0201 0.0165 0.1711 0.1360 0.1068 0.1501 -12.27% 40.54%

146 358 0.6795 0.7017 0.4394 0.4775 0.4907 0.6255 -10.86% 27.47%

114 138 0.0518 0.0756 0.2381 0.2559 0.2615 0.2525 -3.44% -3.44%

112 291 0.0041 0.0188 0.3876 0.3515 0.3194 0.3779 -2.50% 18.32%

130 286 0.2301 0.2937 0.4184 0.5477 0.5749 0.5632 -2.04% -2.04%

102 64 0.2127 0.2308 0.1119 0.1373 0.1505 0.2287 -0.91% 51.96%

128 381 0.0915 0.0751 0.2583 0.3710 0.4190 0.4156 -0.81% -0.81%

150 458 0.4618 0.4994 0.3362 0.4432 0.4566 0.4985 -0.18% 9.18%

115 165 0.4053 0.4383 0.3262 0.3633 0.3720 0.4407 0.55% 18.47%

113 206 0.0531 0.0768 0.2700 0.3304 0.3030 0.3367 1.91% 11.12%

119 326 0.0644 0.0843 0.2442 0.2252 0.2018 0.2497 2.25% 23.74%

107 98 0.1101 0.1580 0.3156 0.4062 0.4451 0.4592 3.17% 3.17%

116 49 0.2635 0.1886 0.3008 0.2803 0.2487 0.3125 3.89% 25.65%

118 273 0.0259 0.0164 0.1551 0.1736 0.1725 0.1806 4.03% 4.70%

145 162 0.3218 0.2983 0.2169 0.2180 0.1961 0.3359 4.38% 71.29%

138 52 0.0895 0.0992 0.1169 0.1321 0.1322 0.1386 4.84% 4.84%

148 250 0.7256 0.7300 0.7316 0.7904 0.8010 0.8416 5.07% 5.07%

108 294 0.0994 0.1266 0.3089 0.2586 0.1820 0.3264 5.67% 79.34%

136 206 0.1713 0.1976 0.3943 0.5899 0.5894 0.6319 7.12% 7.21%

134 188 0.4033 0.4132 0.5091 0.4897 0.4675 0.5500 8.03% 17.65%

132 201 0.1979 0.3246 0.5671 0.5759 0.5625 0.6222 8.04% 10.61%

133 80 0.2396 0.2793 0.1541 0.2322 0.2425 0.3034 8.63% 25.11%

147 315 0.1371 0.1266 0.2312 0.2820 0.3001 0.3317 10.53% 10.53%

106 201 0.0354 0.0458 0.2784 0.3463 0.3653 0.4039 10.57% 10.57%

126 240 0.2766 0.2990 0.1997 0.3132 0.3548 0.3971 11.92% 11.92%

125 169 0.1632 0.1719 0.2580 0.2224 0.1842 0.2920 13.18% 58.52%

137 158 0.3365 0.4135 0.2339 0.3128 0.3555 0.4715 14.03% 32.63%

131 28 0.0659 0.0648 0.0671 0.0984 0.1061 0.1255 18.28% 18.28%

110 496 0.4909 0.4968 0.4845 0.4768 0.4336 0.6001 20.79% 38.40%

142 660 0.4269 0.4479 0.4060 0.4694 0.4629 0.5721 21.88% 23.59%

117 275 0.1165 0.1030 0.1296 0.1596 0.1637 0.2007 22.60% 22.60%

149 133 0.0309 0.0836 0.0490 0.0732 0.0769 0.1028 22.97% 33.68%

129 207 0.3391 0.2338 0.2402 0.3348 0.3477 0.4369 25.65% 25.65%

144 49 0.2490 0.2275 0.0832 0.1327 0.1958 0.3130 25.70% 59.86%

105 54 0.0656 0.1744 0.1304 0.1439 0.1441 0.2262 29.70% 56.97%

139 55 0.0911 0.1139 0.0592 0.0705 0.0776 0.1481 30.03% 90.85%

111 285 0.3795 0.3540 0.1991 0.3025 0.3756 0.5025 32.41% 33.79%

123 435 0.0697 0.0800 0.2254 0.2252 0.2043 0.3014 33.72% 47.53%

120 83 0.0165 0.0136 0.0490 0.0556 0.0538 0.0746 34.17% 38.66%

109 742 0.0875 0.0879 0.0811 0.1344 0.1500 0.2290 52.67% 52.67%

141 36 0.0084 0.0125 0.0538 0.0517 0.0514 0.0873 62.27% 69.84%

Avg 10760 0.2035 0.2159 0.2205 0.2543 0.2573 0.3206 24.60% 24.60%

252
Machine Learning for Knowledge-Based Document Routing

(A Report on the TREC-2 Experiment)

Richard M. Tong, Lee A. Appelbaum

Advanced Decision Systems

(a division of Booz»Allen & Hamilton, Inc.)

1500 Plymouth Street, Mountain View, CA 94043

1 Introduction

This paper contains a description of the experi- ments performed by Advanced Decision Systems as part of

the Second Text Retrieval Conference (TREC-2).^

The overall system we have developed for TREC-2

demonstrates how we can combine statistically-oriented

machine learning techniques with a commercially available knowledge-based information retrieval system. As in TREC1, the tool we using for the fully automatic construction of

routing queries is based on the Classification and Regression

Trees (CART) algorithm.^ However, in a departure from

TREC-1, we have expanded our definition of what constitutes a document "feature" within the CART algorithm, and

also explored how the CART output can be used as the basis of topic definitions that can be interpreted by the TOPIC®

retrieval system developed by Verity, Inc. of Mountain View,

CA.

Section 2 of the paper contains a review of the CART algorithm itself and the data structures it produces.

Section 3 of the paper describes the two basic algorithms we

have devised for converting CART output into TOPIC readable files. Section 4 of the paper contains a description of our

experimental procedure and an analysis of the official results, as well as data from a series of auxiliary experi- ments. Section 5 contains some general conunents on overall performance. We conclude in Section 6 with a brief discussion of possible future research directions.

1. Requests for further information about the TREC-2 experiments

should be directed to the authors at the address above, or electroni- cally to either rtong@ads . com or lee@ads . com.

2. A comprehensive discussion of the CART algorithm can be found in [1]. Details of previous work on the use of CART for information retrieval are presented in [2] and in [3]

.

2 The CART Algorithm

CART has been shown to be useful when one has

access to datasets describing known classes of observations,

and wishes to obtain rules for classifying future observations

of unknown class—exactly as in the document routing problem. CART is particularly attractive when the dataset is "messy" (i.e., is noisy and has many missing values) and

thus unsuitable for use with more traditional classification techniques. In addition, and particularly important for the document routing application, if it is important to be able to specify both the misclassification costs and the prior probabilities of class membership then CART has a direct way of

incorporating such information into the tree building process. Finally, CART can generate auxiUary information, such

as the expected misclassification rate for the classifier as a whole and for each terminal node in the tree, that is useful

for the document routing problem.

2.1 CART Processing Flow

Figure 1 shows how the CART algorithm is used to construct the optimal classification tree based on the training data provided to it.The diagram shows the four sub-processes used to generate the optimal tree, T*. The "raw" train- ing data (i.e., the original texts of the articles), together with

the class specifications (i.e., the training data relevance judgments) and the feature specifications (i.e., the words defined

to be features), are input to the Feature Extraction Module.

The output is a set of vectors that record the class membership and the features contained in the training data. These

vectors, together with class priors and the cost function

(these are optional), are input to the Tree Growing Module

which then constructs the maximal tree (T^ja,) that characterizes the training data. Since this tree overfits the data, the next step is to construct a series of nested sub-trees by pruning Tn^ax to the root. The sequence of sub-trees (Tj^^x > >

... >T„) are input to the Tree Selection Module which per253
CART

Training Vectors

Raw

Training Data

Feature

Extraction

Largest

Tree(Tn^a^)

Tree Growing

I

Class Specs.

Feature Specs.

Tree

Pruning

Tree

Selection

Optinnal Tree (T*)

Class Priors Cost Function

Nested Sub-Trees

n'

Figure 1: CART Processing Flow

forms a cross-validation analysis on the sequence and

selects that tree with the lowest cross-validation error.^ This

isT*.

In our TREC-2 system, we take T* and convert it into a TOPIC outline file as described in Section 3. That is, rather than use CART itself to perform the routing on the unseen documents (as we did in TREC-1), we use the CART trees as skeletons for TOPIC concepts, and then have

TOPIC do the routing. An advantage of this is that we can make use of TOPIC'S extensively optimized text database

capabilities, thus allowing us to easily generate the output

files needed for the official scoring program.

2.2 Data Structures Generated by CART

To illustrate the processing that CART performs, we will use an example taken from the TREC-2 corpus. A

example query is as follows:

<top>

<head> Tipster Topic Description

<num> Number: 097

<dom> Domain: Science and Technology

<title> Topic: Fiber Optics Applications

<desc> Description:

Document must identify instances of fiber

optics technology actually in use.

To be relevant, a document must describe

actual operational situations in which

fiber optics are being employed, or will

be employed. A document describing future

fiber optics use will be relevant only if

contracts have been signed concerning the

future application.

<con> Concept (s)

:

1. fiber optic, light

2. telephone, LAN, television

<f ac> Factor ( s )

:

<def> Definition (s)

:

1. Fiber optics refers to technology in

which information is passed via laser

light transmitted through glass or plastic

fibers

.

<\top>

This is a very comprehensive statement of infor- mation need and provides a rich set of features that we can

use for CART.'* Our basic procedure is to extract from the information need statement all the unique content words

and then stem them, which gives the following list:

ACT APPL CONCERN CONTRACT DESCRIB DOCU

EMPLOY FIB FUT GLASS INFORM LAN LAS LIGHT

OPER OPT PAS PLAST REF RELEV SIGN SITU

TECHNOLOG TELEPHON TELEV TRANSMIT VIA^

<narr> Narrative:

3. The algorithm actually minimizes with respect to both the cross-validation error and the tree complexity. So that if two trees have statistically indistinguishable error rates, then the smaller of

the two trees will be selected as optimal.

4. In general, determining what set of features to use for CART is a matter of experience and judgement. For the TREC-2 corpus we

have made use of the information need statements, but other approaches, such as using all the unique words in the training set, are equally vaUd. In fact, it is this freedom of choice of features

that gives CART a great deal of its flexibility.

254
Given this list of features we can generate tlie CART training vectors by testing all the documents in the

training set for the presence of absence of the feature.^ The

resulting vectors, together with the ground truth classification, are the input to CART. The output from CART contains information about the sequence of nested decision

trees and a specification of the optimal tree.

For our example, the maximal tree is shown in Figure 2. This tree is to be read from left to right. Thus the

Associated with each decision node is information

that tells:

what level in the tree the test occurs; the k value —

in all cases k ranges from k=l, which corresponds to the maximal tree, to ]<i='k^^y, which corresponds to the null tree and where kf^ia^ is dependent on the actual tree grown by CART;

class 0 (k;= CONCERN<=0 . 50 ( class

TRANSMIT<=

class

GLASS<=0.50 {k=5,ac=

class

TRANSMIT<=

class

CONCERN<=0.50 ( class 0 (k

VIA<=0.50 (k=5,ac=0,Rt=0.

class 1 (k=4,Rt

TRANSMIT<=0.50 (k=5,

class 0 (k=4,Rt

SIGN<=0.50 {k=5,ac=0,Rt=0.3454

class 1 (k=5,Rt=0. 003917)

LIGHT<=0 . 50 (k=6,ac=0, Rt=0 . 37 6884)

class 0 (k=3,Rt=0. 000000)

GLASS<=0.50 {k=5,ac=0,Rt=0.031

class 0 (k=3,Rt=0.01

TRANSMIT<=0.50 (k=3,ac=0,

class 0 {k=3,Rt

VIA<=0.50 {k=3,ac=l,

class 1 {k=3,Rt

CONTRACT<=0 . 50 (k=7 , ac=l , Rt=0 . 497487)

class 1 {k=4,Rt; = 0. 019586)

SIGN<=0.50 (k=4,ac=l,Rt=0.0235

class 0 {k=4, Rt=0 . 000000)

LIGHT<=0 . 50 (k=6,ac=l,Rt=0 .027421)

class 0 (k=4,Rt=0. 000000)

2, Rt=0. 230318)

k=2,ac=0,Rt=0. 251256)

1 (k=2,Rt=0. 019586)

0.50 (k=2,ac=0,Rt=0. 020938)

0 (k=2,Rt=0. 000000)

0,Rt=0. 261725)

1 (k=2,Rt=0. 007834)

0.50 (k=2,ac=0,Rt=0. 010469)

0 (k=2,Rt=0. 000000)

k=2,ac=0,Rt=0. 010469)

=2, Rt=0. 000000)

303601)

=0.003917)

ac=l,Rt=0. 007834)

=0.000000)

77)

407)

0469)

Rt = 0. 031407]

=0.010469)

Rt=0. 0195861

=0.003917)

03)

Figure 2: Maximal Tree

first test is on the presence of the stem contract . If the stem is present (i.e., if the test CONTRACT<=0 .5 fails) then the tree branches downwards (this is leftward in CART

jargon); if the stem is not present (i.e., the test succeeds)

then the tree branches upwards (rightward).

5. We only use the <nan>, <con> and <defi> flelds, and the stop word list is taken from [4]. The stemming algorithm is taken from

[5].

6. In our TREC-1 experiments we also made use of the frequency

of occurrence of the features in the documents. Since our ultimate goal here is to generate TOPIC trees we restrict ourselves to just

testing for the presence or absence of the feature — this allows us

to perform a straightforward conversion between CART and TOPIC.

• the class to be assigned to this node if the tree were

pruned here; the ac value — in the present case ac=l for the class that corresponds to a relevant document and ac=0 for the class that corresponds to a

non-relevant document; and

• the error rate of the node; the Rt value — this indi- cates the resubstitution error rate for the specific

node.

The terminal nodes in the tree have similar infor- mation associated with them. Notice though that termmal

nodes, by definition, specify to which class the node corre- sponds.

255
CART also generates the following table of error estimates that are used in selecting the optimal tree. Here k

is the k-value, I T I is the size of the tree (measured in terms

of the number of terminals), R ( T ) is the resubstitution rate for the overall tree, and Rev ( T ) is the cross-validation rate for the overall tree. Recall that CART minimizes with

respect to both size and cross-validation rate, so that for our

example, the optimal tree is when k=4.

^ j^(,p) Rcv(T) so that the optimal tree is as shown in Figure 4.

3 20 0 1313 0 2935

4 10 0 1761 0 2758

5 7 0 1971 0 2815

*6 6 0 2050 0 2929

7 5 0 2154 0 3101

8 4 0 2273 0 3101

9 2 0 2681 0 3328

10 1 0 4975 0 6264

1 16 0 3100 0 3037

2 11 0 3140 0 2866

3 8 0 3206 0 2353

*4 5 0 3323 0 2296

5 2 0 4043 0 3891

'6 1 0 4975 0 6285

So, pruning the maximal tree so that all nodes have k>4

gives us the following optimal tree shown in Figure 3.

3 Transforming CART Trees into TOPIC

Outline Specifications

The trees produced by CART are not directly

usable by TOPIC because they represent the information we

need (i.e., the decision function and the associated decision

variables) in a form that is incompatible with the TOPIC

class 0 (k=5,Rt=0. 261725)

VIA< = 0.50 (k;=5,ac=0,Rt=0. 303601)

class 1 (k=5,RC=0. 007834)

SIGN<=0.50 (k=5,ac=0,Rt =0.34 5477)

class 1 (k=5,Rt=0. 003917)

LIGHT<=0 . 50 (k=6, ac=0, RC=0 .37 6884)

class 0 {k=5,RC=0. 031407)

CONTRACT<=0 . 50 (k=7 , ac=l , Rt=0 .497487)

class 1 (k=6,Rt=0. 027421)

Figure 3: Optimal Tree Using Stemmed Features

Notice that by pruning away the deeper nodes in the tree we

are left with a tree that tests on just five of the original 26

stems, and has three paths that lead toclass-1 nodes (i.e., a decision that the document is relevant).

The unstemmed version of our procedure is identi- cal except that we do no stemming of the unique words

extracted from the information need statement. For the example, this produces the following list of features:

ACTUAL APPLICATION CONCERNING CONTRACTS

DESCRIBE DESCRIBING DOCUMENT EMPLOYED

FIBER FIBERS FUTURE GLASS INFORMATION LAN LASER LIGHT OPERATIONAL OPTIC OPTICS

PASSED PLASTIC REFERS RELEVANT SIGNED

SITUATIONS TECHNOLOGY TELEPHONE TELEVISION

TRANSMITTED VIA

and the following error table:

k ITI R(T) Rcv(T)

1 36 0.0932 0.3168

2 33 0.0972 0.2763

knowledge-representation. The main problem then is to define a transformation from the CARTrepresentation to the TOPIC representation that at least preserves the decision

information and perhaps augments it so that we get

improved routing performance.

In this section we explore two possible strategies for constructing these transformations. The first is a strict re-coding

of the information in the CART tree. The second generalizes

the intent of the CART tree but adds no new information.

Both of these techniques are "automatic,!' in the sense that, once various parameters have been chosen, the algorithms

work without human intervention.

3.1 First Canonical Form

The first canonical from completely preserves the decision function generated by CART and involves a simple

mapping of the CART tree into TOPIC outline file format.^ To do this, we begin by observing that each path in the CART tree from the root toaclass-1 leaf node constitutes a conjunction of tests of decision variables. Since we

256
class 0 (k=10,Rt=0. 062814)

TELEPHONE<=0. 50 (k=ll , ac=l, Rt=0 . 497487

)

class 1 (k=8,Rt=0. 142139)

TRANSMITTED<=0 . 50 { k=9 , ac= 1 , Rt =0 .153 984)

class 0 (k=8, Rt=0 . 000000)

LIGHT<=0. 50 {k=10,ac=l,Rt=0.205312)

class 0 (k=7,Rt=0. 000000)

TRANSMITTED<=0. 50 ( k=9 , ac=0 , Rt =0 . 0 1 04 69

)

class 1 {k=7,Rt=0. 000000)

ACTUAL<=0. 50 (k=9 , ac=0 , Rt =0 . 03 1407

)

class 1 (k=9, Rt=0 . 000000)

Figure 4: Optimal Tree Using Unstemmed Features

have constrained the tests to be of the form "Is word X

present or not?" we can easily model this conjunction in TOPIC using AND and NOT operators. Since there will generally be multiple paths toclass-1 leaf nodes, the algorithm combines the separate paths in the TOPIC definition using the OR operator.

To illustrate, the optimal tree for our example contains the following conjunctive descriptions of class-1 leaf nodes:

1. CONTRACT

2 . SIGN and not LIGHT and not CONTR/.CT

3. VIA and not SIGN and not LIGHT and not CONTRACT

which leads directly to a TOPIC outline of the form:

Topic_097 <0r>

* 0.77 TopicStyle_097_l <0r>

** 0.99 TopicPath_097_l-l <And>

*** ~ 'CONTRACT'

*** ~ 'LIGHT'

*** ~ 'SIGN'

*** 'VIA'

** 1.00 TopicPath_097_l-2 <And>

*** ~ 'CONTRACT'

*** ~ 'LIGHT'

*** 'SIGN'

** 0.97 TopicPath_097_l-3 <And>

* * * ' CONTRACT

Here we use a notation for topic names that ensures

uniqueness. Notice that in this model we use the resubstitution rates (actually l-Rj) to define a weight for the individ- ual conjuncts and the overall cross-validation rate (actually

1-Rcv) as the weight for the disjuncts. Note also that since

TOPIC only uses weights defined to two decimal places we

have rounded the weights derived from the CART tree.

7. TOPIC uses a representation for concepts that can be recorded

in so-called outline format. A collection of such specifications is used by TOPIC to built the concept trees used for retrieval.

3.2 Second Canonical Form

The second canonical form maJces use of the deci- sion variables chosen by CART but not the actual decision

function. This model is based on two observations:

• first, that the set of variables used by CART are, when taken as a whole, indicative of the general topic

of the information need statement, and

• second, that every variable used in the tree is on the path to at least one class-0 node and at least one class-1 node.

Thus, from an information retrieval perspective, all the decision variables have some contribution to make to an

assessment of the relevance of a document. So rather than use the specific decision function constructed by CART we

can replace this with one that can be thought of as 'The more features present in a document the better." This is modelled straightforwardly in TOPIC using the ACCRUE

operator.

To illustrate, the maximal tree for our example

contains the following set of decision variables:

CONCERN CONTRACT GLASS LIGHT SIGN TRANSMIT

VIA

which leads directly to a TOPIC outline of the form:

Topic_097 <0r>

* 0.70 TopicStyle_097_2 <Accrue>

** 0.2 5 'CONCERN'

** 0.7 5 'CONTRACT'

** 0.75 'GLASS'

** 0.7 5 'LIGHT'

** 0.75 'SIGN'

** 0.7 5 'TRANSMIT'

** 0.75 'VIA'

The weighting scheme we have used gives higher values to variables (features) that are in the optimal tree, intermediate values to variables on the fringe of the optimal tree (there

257
are none of these in the current example), and lower values

to features outside the optimal tree.^ At this point the specific values chosen represent our "best guess" at a weighting

scheme, further experimentation will undoubtedly reveal a

better strategy. As in the first canonical form, the overall weight for the TOPIC tree is based on the cross-validation

rate for the maximal tree.

4 The TREC-2 Experiments

For TREC-2 we again focused only on the document routing problem. Since our technique requires training data it does not easily lend itself to the ad hoc retrieval problem and so rather than "force-fit" it we chose to generate four sets of results for the routing queries (topics 51-

100). Each set of results was generated totally automatically. The results sets are labelled adsl, ads2, ads3, and

ads4, and the table below shows to which combuiations of

features and TOPIC models they correspond.

Table 1: Results Identification

Result Set Word Features TOPIC Model

adsl stemmed model1

ads2 unstemmed model1

ads3 stemmed model-2

ads4 unstenmied model-2

Although we generated four sets of results, the resource constraints at NIST resulted in only adsl and ads2

being officially scored. Reference in the remainder of the paper to scores associated with ads3 and ads4 are to the

unofficial score generated by us using the TREC-2 scoring

program and the published qrels for the routing topics.

4.1 The Experimental Procedure

The experimental procedure for TREC-2 consists of five basic steps. We briefly describe each of these:

• First, we generated the CART training data from the information need statements and the ground truth

files (i.e., the qrels) provided by NIST. This produced

two feature sets for each topic (corresponding to the stemmed and unstemmed versions of the features).

8. A variable is in the optimal tree if its k-value is greater than k*;

is on the fringe if k=k*; and outside the optimal tree if k<k*. Note

that in general the individual features appear at multiple locations

in the tree. Our strategy is to remove duplicates by retaining the instance with the highest k-value.

and two sets of training vectors labelled with the ground truth information.^ Since CART is a statisti- cally-oriented classifier, we decided to minimize the "noise" in the training sets by using only the Wall

Street Journal articles identified in the qrel files. Further, for all but topics 80 and 81, we used just the Wall Street Journal articles on Disk 2.

• Second, we grew the CART trees from this training

data. Since we had two sets of training data for each

topic, we grew two trees for each topic.

• Third, we used the algorithms described in Section 3

to convert the CART trees into a TOPIC readable

form. This produced four TOPIC definitions for each

of the information need statements. Table 1 above

shows the various combinations.

• Fourth, we ran the TOPIC definitions against the indexed unseen data.'^ Again, to minimize noise

effects, we used only the Associated Press articles on

Disk 3 to generate our official results.

• Fifth, we sorted and merged the results generated by

TOPIC and converted them into the TREC format for scoring by NIST.

4.2 Discussion of Official Results

The official results for adsl and ads2, together with

the unofficial results for ads3 and ads4, are shown in Table 2.

Table 2: TREC-2 Results (AP Only)

Run No. No. Rel. Av. Exact ID Retr. Rel. Ret. Prec. Prec.

adsl 40,423 5,677 822 0.0195 0.0390

ads2 33,034 5,677 1,468 0.0821 0.1092

ads3 49,006 5,677 1,182 0.0168 0.0374

ads4 50,000 5,677 1,847 0.0630 0.0868

The first observation is that the trees built using exact words as features (i.e., results ads2 and ads4) had

higher precision than those built using word stems. We

9. The feature specification and extraction procedure we used is identical to that used in TREC-1 and is described in detail in the TREC-1 proceedings. The only differences are the addition of a stemmed version of the features and the fact that we do not make

use of the feature count information.

10. We are grateful to Verity Inc. for allowing us to have access to their computer systems and databases.

258
believe that the explanation for this is that by using

stemmed versions of the features we added a significant

amount of "noise" to the sample space. That is, given the

relatively small size of the training sets, using stems tends to reduce the discriminating power of any given feature with

respect to the training sets. This manifests itself indirecdy in two ways. First, the optimal trees built with stems are generally smaller than those built using exact words; and, second,

optimal trees built with stems have higher cross-vahdation

error rates than those built using exact words.

The second observation is that the TOPIC trees

built using model-2 (i.e., results ads3 and ads4) had better

recall than those built using model- 1. The explanation for

this is straightforward. Since the model-2 trees essentially use all the features extracted from the information need

statement in a generahzed disjunction, they provide much

broader coverage than the model- 1 trees which often use

just one or two of the features.

The third observation is, of course, that these are not strong results since on average all the models performed

in the low end of Uie scores reported by NIST in the sum- mary routing table. This is somewhat disappointing since our results in TREC-1 led us to believe that we might be

able to do significantly better.

Notwithstanding the fact that we did not explore some of the ideas discussed in the TREC-1 paper (e.g., the use of concepts rather than words as features, and the use of

surrogate split information), we are now inclined to the view that the output from tools like CART are best used as

the basis for manually constructed routing topics. To begin

to explore this idea, we performed a number of auxiliary

tests that we report in the following section.

4.3 Auxiliary Experiments

To explore the idea of using the CART output as

the "skeleton" for a manually constructed routing query, we

selected two model-2 trees to determine whether a minimal

set of "edits" could significantly improve their performance.

We selected Topics 52 and 54 since they represented one

topic for which the automatically generated tree did well

(Topic 52) and one for which the automatically generated

tree did poorly (Topic 54).

The scores for Topic 52 (for the AP corpus only)

are shown below:

11. We do note, however, that for a number of topics we did rather well in comparison with other systems (i.e.. Topics 51 and 75), and

that in absolute terms we produced a number of trees that had

greater than 30% R-precision (i.e., for Topics 52, 58, 78 and 93).

Queryid (Num) : 52 Total number of documents over all queries

Retrieved: 1000

Relevant: 345

Rel_ret: 328

Interpolated Recall - Precision Averages:

at 0.00 0.6667

at 0 . 10 0 . 4684

at 0.20 0.4032

at 0.30 0.4032

at 0.40 0.4032

at 0.50 0.4032

at 0.60 0.4013

at 0.70 0.4013

at 0.80 0.4013

at 0.90 0.4003

at 1.00 0.0000

Average precision (non- interpolated) over

all rel docs:

0 . 3828

Precision

At 5 docs 0 4000

At 10 docs 0 3000

At 15 docs 0 4667

At 20 docs 0 6000

At 30 docs 0 6333

At 100 docs 0 4100

At 200 docs 0 3550

At 500 docs 0 3820

At 1000 docs 0 3280

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

:

Exact: 0.3739

Here we see that this topic tree does well —recall is excellent and precision is sustained even at high recall levels.

In contrast, the topic tree for Topic 54 produces the following results:

Queryid (Num) : 54 Total number of documents over all queries

Retrieved: 1000

Relevant: 65

Rel_ret : 64 Interpolated Recall - Precision Averages:

at 0 00 0 1323

at 0 10 0 1323

at 0 20 0 1323

at 0 30 0 1323

at 0 40 0 1323

at 0 50 0 1323

at 0 60 0 1323

at 0 70 0 1102

at 0 80 0 1102

at 0 90 0 1097

at 1 00 0 0000

Average precision (non-interpolated) over

all rel docs:

0 . 0907

259
Precision:

At 5 docs

:

0 0000

At 10 docs : 0 0000

At 15 docs : 0 0000

At 20 docs

:

0 0000

At 30 docs : 0 0000

At 100 docs : 0 0500

At 200 docs : 0 0600

At 500 docs : 0 1080

At 1000 docs

:

0 0640

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

:

Exact: 0.0308

Thus although recall is very good, precision is completely

unsatisfactory.

Our conjecture is that the automatically constructed model-2 trees while generally giving good recall give poor precision because they contain many extraneous

features, or features that should be combined. To illustrate

this, we considered the modeI-2 tree for Topic 52, as a start- ing point for a manually constructed tree. The initial model2 tree is:

Topic_52 <0r>

* 0.86 Topic_Style_52_2 <Accrue>

* * 0 75 "AFRICA

* * 0 25 "AFRICAN"

* 0 75 "APARTHEID"

* * 0 25 "ARMS"

* * 0 25 "BAN"

* * 0 25 "BLACK"

* * 0 25 "COMPANY"

* * 0 25 "COMPLIANCE"

* * 0 25 "CONTRACTS"

* * 0 25 "CORPORATE

* * 0 25 "DISCUSS"

* * 0 25 "DOCUMENT"

* * 0 50 " DOMINATION"

* * 0 25 "GOVERNMENT

* * 0 25 " INTERNATIONAL

* * 0 25 "INVESTMENT"

* * 0 25 "ORGANIZATION"

* * 0 25 "PRESSURE"

* * 0 75 "PRETORIA"

* * 0 25 "REDUCTION"

* * 0 25 "RESPONSE"

* * 0 75 " SANCTIONS

* * 0 75 "SOUTH"

* * 0 25 "TIES"

* * 0 25 " TRADE

* * 0 25 "UNITED"

Obviously there are a number of features here that are basically "noise" — for example the words "COMPANY" and

"RESPONSE"; and other words are clearly elements of a

larger phrase — for example the words "SOUTH" and

"AFRICA". Notice that, in general, words with lower

scores are always candidates for elimination.

The result of this pruning exercise was the following revised definition for Topic 52:

Topic_52 <0r>

* 0.86 TopicStyle_52-2 <Accrue>

** 0.50 S_Africa <Accrue>

*** 0.50 'SOUTH AFRICA'

*** 0.50 "PRETORIA"

** 0.50 'SANCTIONS'

** 0.20 Topic_52_Support <Accrue>

*** 0.50 "APARTHEID"

*** 0.50 <Near>

  'BAN' ** ** 'TRADE'

*** 0.50 <Near>

  'BAN' ** ** 'INVESTMENT'

So although we have added no new features, we have combined "SOUTH" and "AFRICA" and used this together

with " PRETORIA" to define a concept called S_Africa.

We have also used "APARTHEID"; and "BAN" with

"TRADE" and " INVESTMENT" to define another concept

called Topic_52_Support. Finally we adjusted the weights to give more prominence to S_Africa than Topic_52_Support.

The results for this modified topic description are:

Queryid (Num) : 52 Total number of documents over all queries

Retrieved: 1000

Relevant: 345

Rel_ret: 312

Interpolated Recall - Precision Averages:

at 0.00 1.0000

at 0.10 1.0000

at 0.20 0.9780

at 0.30 0.9766

at 0.40 0.9603

at 0.50 0.9067

at 0.60 0.8620

at 0.70 0.8620

at 0.80 0.8620

at 0.90 0.7422

at 1.00 0.0000

Average precision (non-interpolated) over

all rel docs:

0.8305

Precision

:

At 5 docs 1 0000

At 10 docs 1 0000

At 15 docs 1 0000

At 20 docs 1 0000

At 30 docs 1 0000

At 100 docs 0 9700

260
At 200 docs:

At 500 docs: At 1000 docs:

0.8900

0.6220

0.3120

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

Exact: 0.8493

So that although recall decreased slightly (we now retrieve 312 rather than 328 of the 345 relevant documents), the precision is improved by nearly 50 percentage points. Obviously, this is a significant improvement and was achieved

with minimal manual input. The time required to make

these changes was only of the order of 10 minutes.

We repeated this exercise with Topic 54, the initial model-2 tree for which is:

Topic_54 <0r>

* 0.93 Topic_Path_54_2 <Accrue>

* *

* *

* * **

0.7 5 "MCDONNELL DOUGLAS'

0.7 5 "GENERAL DYNAMICS"

LAUNCH"

MARTIN MARIETTA"

PAYLOAD"

0.75 "ROCKET"

0.7 5 "SATELLITE"

SPACE" TITAN" CONTRACT

•

LAUNCH SERVICE'

0.10

0.75

0.75

0.75

0 . 50 0.75

0.75

Notice that here we actually added words that were part of

obvious proper names (i.e., the "GENERAL" of "GENERAL

DYNAMICS", the "MARIETTA" of "MARTIN MARIETTA", and the "MCDONNELL" of "MCDONNELL DOUGLAS"), but otherwise nothing was added. We also adjusted

the weights on 'AGREEMENT' and "LAUNCH" to de- emphasize their importance.

* * A

U / D 0 1 5 " ARIANE

The results of running this modified query are:

* * 0 75 "ARIANESPACE"

** 0 75 "ATLAS" Queryid (Num) 54

* * 0 75 "COMMERCIAL" Total number of documents over all queries

* * 0 75 "CONTRACT" Retrieved: 1000

* * 0 75 "DELTA" Relevant

65

* * 0 75 "DOCUMENT" Rel_ret

65

* * 0 75 " DOUGLAS" Interpolated Recall - Precision Averages:

* * 0 75 "DYNAMICS" at 0.00 0 . 5800

* * 0 75 "INDUSTRY" at 0.10 0 . 5800

* * 0 75 " LAUNCH

at 0.20 0 . 5800

* * 0 75 "MARTIN" at 0.30 0 . 5800

* * 0 75 "MENTION" at 0.40 0 . 5800

* * 0 75 " PAYLOAD" at 0.50 0 .4592

* * 0 75 " PRELIMINARY" at 0 . 60 0 .4592

* * 0 75 " RELEVANT" at 0.70 0 . 4324

* * 0 75 " RESERVATION" at 0 . 80 0 . 3355

* * 0 75 "ROCKET

at 0.90 0 . 1474

* * 0 75 " SATELLITE

at 1 .00 0 .0657

* * 0 75 "SERVICES" Average precision ( non-interpolated) over

* * 0 75 "SPACE" all rel docs

* * 0 75 "TENTATIVE" 0 . 3889

* * 0 50 "TITAN" Precision

Using the same kinds of procedures (i.e., removing

extraneous words and combining words into phrases) we

constructed the following modified tree:

Topic_54 <0r>

* 0.93 Topic_Path_54_2 <Accrue>

0.10 'AGREEMENT'

0.7 5 "ARIANE"

0.75 "ARIANESPACE"

0.75 Atlas_Rocket <Sentence>

"ATLAS"

" ROCKET"

0.75 Commercial_Satellite <Sentence>

"COMMERCIAL"

"SATELLITE"

0.7 5 "DELTA II"

* * * *

* *

* *

* * *

* * * * * * * *

* * *

At 5 docs 0 2000

At 10 docs 0 1000

At 15 docs 0 3333

At 20 docs 0 4000

At 30 docs 0 4667 At 100 docs 0 4500

At 200 docs 0 2700

At 500 docs 0 1260

At 1000 docs 0 0650

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

Exact: 0.4615

So that, again for very little manual input, we achieved a

significant improvement in precision performance; and this time at no cost to recall.

261
It is interesting to compare our results with Verity's scores for these two topics. To do this we re-scored Verity's

T0PIC2 results on die AP corpus alone.'^ For Topic 52,

Verity's results were:

Queryid (Num) : 52 Total number of documents over all queries

Retrieved: 1000

Relevant: 3 45 Rel_ret: 317

Interpolated Recall - Precision Averages:

at 0.20 0.9130

at 0.30 0.9130

at 0.40 0.9000

at 0.50 0.7609

at 0.60 0.5942

at 0.70 0.5679

at 0.80 0.5049

at 0.90 0.2027

at 1.00 0.0927

Average precision (non-interpolated) over

all rel docs:

at 0 00 1 0000 0 6838

at 0 10 0 9833 Precision

at 0 20 0 9342 At 5 docs : 1 0000

at 0 30 0 8607 At 10 docs : 0 8000

at 0 40 0 8314 At 15 docs : 0 8667

at 0 50 0 7425 At 20 docs : 0 9000

at 0 60 0 7125 At 30 docs : 0 9000

at 0 70 0 6704 At 100 docs : 0 5100

at 0 80 0 6161 At 200 docs : 0 2900

at 0 90 0 3952 At 500 docs : 0 1280

at 1 00 0 0000 At 1000 docs : 0 0650

Average precision (non- interpolated) over

all rel docs:

0.7159

Precision

:

At 5 docs 1 0000

At 10 docs 1 0000

At 15 docs 1 0000

At 20 docs 1 0000

At 30 docs 1 0000

At 100 docs 0 9000

At 200 docs 0 7900

At 500 docs 0 5820

At 1000 docs 0 3170

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

:

Exact: 0.5846

This shows the same recall performance (i.e., all 65 relevant documents were retrieved) but substantially better precision

performance. Through the first 30 documents T0PIC2 gave

excellent results, whereas our modified model-2 result was

only half as good. Again however, the T0PIC2 tree is much

more complex, and required more effort to develop 13

R-Precision (precision after R (= num_rel

for a query) docs retrieved)

:

Exact: 0.6812

Here we see better recall (317 of the 345 relevant documents retrieved) but with sUghtly lower precision. The

T0PIC2 tree for this topic is much more complex than the one we developed, which explains the better recall. Notice

however that both trees gave perfect precision for the first 30 documents.

For Topic 54, Verity's T0PIC2 results were:

Queryid (Num) : 54 Total number of documents over all queries

Retrieved: 1000

Relevant: 65 Rel_ret: 65

Interpolated Recall - Precision Averages:

at 0.00 1.0000

at 0.10 0.9130

Overall, we are impressed by the improved perfor- mance we were able to achieve with minimal manual effort. These auxiliary experiments provide at least suggestive evi- dence of the value of automatic generation of initial trees. The extent to which this is consistently achievable will require further investigation, and we hope to report on this inTREC-3.

5 Commentary

The official results of our TREC-2 experiments

demonstrate that automatic consuuction of routing queries

from training documents is indeed feasible. The queries pro- duced are in fact binary classification trees that are optimal

with respect to size (measured in terms of the number of ter- minals in the ttee) and the estimated error rate of the tree. Unfortunately, however, these trees generally appear to have poor performance. In a few cases the trees were comparable with the results from other sites, but they mostly

12. We are grateful to Verity for allowing us to examine their TREC-2 results in detail.

13. We do not have precise figures for the amount of effort needed

to build the Verity TOPIC2 trees, but in general each topic required

several hours of effort.

262
seemed unsatisfactory. We have not been able to identify

any specific conditions under whicli we could expect the CART trees to perform well.

We believe, nevertheless, that further experiments

to assess the utility of using a variety of extensions to the

basic CART technique would still be of interest. In particular, the use of "low-level" concepts as features, surrogate

split information, larger training sets, and features drawn

from the complete corpus rather than the information need

statements, are all likely to assist in the construction of more

effective trees.

The main focus of our TREC-2 effort has been to explore the idea that the CART trees can form the basis of a semi-automated approach to building knowledge-based

descriptions of routing topics. To that end, we developed

two techniques for converting CART output into a form that can be used by the TOPIC retrieval engine from Verity, Inc. In the first technique we perform a "lossless" transformation

of the CART tree into a TOPIC tree. In the second we generalize the CART tree, although add no new features.

As the examples contained in the paper show, the TOPIC trees generated in the first canonical form are "skeletal." This is just as we would expect. Since CART is a par- simonious classifier, rather than a broad-based information

retrieval tool, it produces minimally complex decision trees with respect to the expected misclassification rate.

From an information retrieval perspective, the sec- ond canonical form seems more Uke the ones we might have

built by hand. It uses a range of features and gives them

weights based on their ability to discriminate among the

training data. In effect, we have used CART to indicate which of the features are of use in defining the topic and

then generalized the CART decision function using our

(external) knowledge of the information retrieval problem.

Using these automatically constructed TOPIC trees

as a starting point, we conducted a limited series of tests to assess the impact of performing minimal "editing" of these

U^ees. For the two topics selected, we were able to produce

significant performance gains witli edits that added no new

information (at least at the level of the features used) and

that took of the order of only a few minutes to implement.

We are encouraged by these results, and, while the

generalization of them will require a more carefully controlled series of experiments, we are now of the opinion that the most effective role for machine learning techniques in information retrieval is as a tool for producing candidate

descriptions of information need. These candidates can then be reviewed by end-users who can easily make obvious corrections and modifications. We intend to explore this idea in more detail and report on our results in TREC-3.

6 Future Research

There a number of directions in which we might

develop the basic research ideas presented in this paper. We

briefly consider a number of them here.

We currently use just two classes (relevant and not

relevant), but nothing in CART prevents it working with

multiple classes. For the document routing problem, there is a case to be made for adding a third class — unknown rele- vance. Adding such a class might allow us to make use of

larger training sets without the costs associated with devel- oping large ground truths.

One way of extending the skeleton TOPIC trees produced by our tool is to make use of external lexical resources. For example, we might investigate the use of

WordNet as a way to expand each of the classification fea- tures into a set of related words. Similarly, we might investi- gate the use of TOPIC'S own lexical resources (e.g., the thesaurus and Soundex tools) by replacing the unstenuned

words in the topic outline files with the appropriate TOPIC

operator (e.g., <SOUNDEX> word, or <THESARUS>

word).

Although we have used CART as the module for building the initial classification tree, we might be interested

in exploring other tree building tools that have been used in the machine learning community. For example, the C4.5

algorithm by Quinlan [6], or various algorithms based on

Bayesian methods such as Minimum Message Length models and decision graphs [7]. All of these tools generate deci- sion trees from training data but offer different mathematical philosophies to justify their approaches.

Finally, as in all machine learning problems, the

initial choice of features over which to learn is extremely

critical to the overall success of the process. An investigation of various extended feature definition tools (e.g., recognizing key phrase and proper names), as well as exploring

the impact of making different assumptions from TOPIC

about how the lexical tokens in the texts are to be treated, would almost certainly yield important insights.

References

1. Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Classification and regression trees. Pacific Grove, CA: Wadsworth & Brooks; 1984.

263
2. Crawford, S. L.; Fung, R. M.; Appelbaum, L.A.; Tong, R. M. Classification trees for information

retrieval. In: Proceedings of the eighth international workshop on machine learning (ML91). San Mateo,

CA: Morgan Kaufmann; 1991.

3. Tong, R. M.; Winkler, A.; Gage, P. Classification

trees for docimient routing. In: Harman, D. K., ed. The first text retrieval conference (TREC-1). NIST

Special Publication SP-500-207, Gaithersburg, MD:

March 1993.

4. Fox, C. A stop list for general text. SIGIR Forum,

24(l,2):19-35; Fall/Winter 1989/90.

5. Paice, C. D. Another stemmer. SIGIR Forum,

24(3):56-61; Fall 1990.

6. Quinlan, J. R. C4.5: Programs for machine learning.

San Mateo, CA: Morgan Kaufinann; 1992.

7. OUver, J. J.; Dowe, D. L.; Wallace, C. S. Inferring

decision graphs using the minimum message length

principle. In: Proceedings 5th Australian joint conf. on AI. 1992.

264
The ConQuest System

Paul E. Nelson

VP of Research & Development

tg^i CONQUEST" SOFTWARE

9705 Patuxent Woods Drive, Columbia, Maryland 21046

(410)-290-6290

Introduction

ConQuest software has a commercially available text search

and retrieval system* called "ConQuest" (for Concept

Quest). ConQuest is primarily an advanced statistical based

search system, with processing enhancements drawn from

the field of Natural Language Processing (NLP).

ConQuest participated in Category A of TREC, and so produced results for 50 test queries over the entire 2.3 Gigabyte database. In this category, we constructed queries

and submitted results for two different ranking functions. These two functions tested the difference between local and

global document relevancy, and are fully described later.

In TREC-2, ConQuest had a very strong showing. Our

recall scores in particular improved by about 1 8 percentage

points over the adjusted TREC-1 scores. Our precision

scores were also very competitive.

The purpose of this paper is to discuss how we prepared for TREC-2: how queries were performed, what initial judgments were made and why, and interpretation of the

results. Then, I will cover the tests which were performed

after TREC-2, and how these tests clearly identify the areas where ConQuest could most effectively be improved.

System Architecture

For a complete discussion of the system architecture of

ConQuest, see the TREC- 1 conference proceedings, or call the author. The following overview is meant as a brief

refresher. ConQuest uses pre-built indexes to perform text database

searches at fast speeds. In such a system, all text to be

searched must first be indexed. These indexes are then used

for all searching; the original document data is not required.

ConQuest uses a dictionary augmented with a semantic

network for both indexing and queries. The dictionary is a

list of words where each word contains multiple meanings.

Each meaning contains syntactic information (part-ofspeech, feature values), and a dictionary definition.

The semantic network contains nodes which correspond to meanings of words. These nodes are linked to other related nodes. Relationships between nodes are extracted from

machine readable dictionaries. Some example relationship

types include synonym, antonym, child-of, parent-of,

related-to, part-of, substance-of, contrasting, and similar-to. The ConQuest dictionary was generated automatically from

several Machine Readable Dictionary (MRDs) sources, commercially available. This gives ConQuest the most

robust and thorough coverage of English available. It is the completeness of coverage that drives performance gains in recall and precision.

Since ConQuest is a commercially available product, many

additional components, not required for TREC-2, are also

available, such as true client/server, graphical user

interfaces, routing and dissemination, and sophisticated

application program interfaces.

Query

Generally speaking, ConQuest attempts to refine and

enhance the user's query. The result is then matched against

the indexes to look for documents which contain similar concepts.

Queries are not "understood" in the traditional sense of

natural language processing. ConQuest makes no attempt

to deeply understand the objects in the query, their interaction, or the user's intent. Rather, ConQuest attempts

to understand the meaning of each individual word and the importance of the word. It then uses the set of meanings

and their related terms (retrieved from the semantic

networks) as a statistical set which is matched against

document information stored in the indexes.

For additional information on ConQuest, please contact

the author.

265
Query

ConQuest

Dictionary

Tokenize

|—^

Morphology

Find Idioms

Query

Enhancement

Remove

Stop Words

Expand

Meanings

I

Search &

Rank

Semantic

Networks

I

Documents

Indexes

Figure 1 The Query Process

The following is a description of the modules used for

query:

• Tokenize: Divides a string of characters into words.

• Morphology: An advanced form of stemming;

attempts to remove suffixes and perform spelling

changes to reduce words to simpler forms which are found in the dictionary. For example, one morphology

rule will take "babies," strip the "ies," add "y," and

produce "baby," which is found in the dictionary.

• Find Idioms: This module finds idioms in the text and indexes the idiom as a single unit. This prevents

idioms such as "Dow Jones Industrial Average" from

getting confused with queries on "industrial history."

Words inside of idioms can still be located

individually, if desired.

• Query Enhancement: The user is given the opportunity to enhance the query for additional improvement in precision and recall. There are many

options available here, but the two most important are

to choose meanings and weight query terms. Choosing a meaning of a word will restrict the expansion of words to only related terms which are relevant to the chosen meanings. This reduces noise in the query. When running in automatic mode,

ConQuest expands all meanings of all words.

Weighting query terms identifies the importance of the various words in the query. These weights are used by

the search engine when ranking documents and

computing document relevance factors.

• Remove Stop Words: Small function words—such as determiners, conjunctions, auxiliary verbs, and small adverbs—are removed from the query.

• Expand Meanings: Words in the query are expanded to include related terms.

• Search and Rank: ConQuest uses an integrated search and rank algorithm (described in the next section)

which considers the relevance rankings of documents

throughout the search process. Since ranking and

search are integrated, the search engine automatically

produces the most relevant documents right away.

Queries can be expanded to a very large number of terms, if desired. If the user wishes for the greatest amount of recall, a 5 word query can be expanded to 200 or 300 related terms. Many other query features are also available in ConQuest,

including wildcards, fiizzy spelling expansion, numeric and

date range searching, boolean, mixed boolean and statistical, fielded searching (a variety of types), and searching over document categories.

Ranking Factors

Ranking and retrieval with ConQuest uses a variety of

statistics and criteria, which are flexible and can be modified

to handle varying requirements. The following are some of

the factors used in ranking:

Completeness: A good document should contain at least one term or related term for each word in the

original query.

Contextual Evidence: Words are supported by their related terms. If a document contains a word and its related terms, then the word is given a higher weight

because it is surrounded by supporting evidence.

Semantic Distance: The semantic network contains

information on how closely two terms are related.

Proximity: A document is considered to be more

relevant if it contains matching terms which occur

close together, preferably in the same paragraph or

sentence.

Quantity: The absolute quantity of hits in the document is also included, but is not as strong a discriminator of relevance as the other factors. ConQuest is the first truly "concept-based" search system to operate over unrestricted domains. If a document contains

the word and some of its related terms, the word is more

likely to be used in the correct context, using the

"contextual evidence" factor above. In this way, ConQuest

can determine word meanings at query tinfie.

Coarse and Fine Grain Ranking

To further improve retrieval speed, ConQuest performs the search in two phases. The first is "coarse-grain." This phase

is integrated with the document search process. Documents

are output from the ConQuest search engine in descending

coarse-grain rank order. To compute the coarse-grain rank for a document, the

statistics for the words contained in the document are combined using the coarse-grain ranking function. The

inputs to this function include the semantic network

266
strength of each word, frequency in query, expansion terms,

inverse document frequency, and query structure. Once a document is found using coarse-grain ranic, a second

phase of relevancy ranking is applied, called "fine-grain"

rank. This second phase uses a different ranking function which has access to more local information within the document. The inputs to this function include all of the

inputs used in coarse-grain ranking, plus word location,

proximity, frequency in document, and document structure.

Query

i

Coarse-Grain Rank

Document List i

Fine-Grain Rank

Combine Scores

Final Document Rank

Figure 2 Fine and Coarse Grain Ranking

In general, the coarse-grain rank of a document represents

global information on the document. It is a score that applies more to the document as a whole. The coarse-grain

rank will be high for a document if it contains a large

number of query words and related terms, ignoring the position of those terms in the document.

The fine-grain rank, on the other hand, represents local information, because the proximity (physical closeness) of

the terms is the strongest contributor. The fine-grain rank

of a document will be high if there is a single strong

reference contained in the document.

As shown in Figure 2 above, the final document score is computed as a combination of the coarse- and fine-grain

scores.

Pre-TREC Experiments

In preparation for TREC-2, ConQuest performed numerous

experiments to improve the coarse-grain ranking algorithms

and data. These experiments included the following:

1 . Statistical word studies (statistical regressions to predict the probability that a document containing a word is relevant)

2. Statistical word-pair studies

3. Various weighting formulae

4. Various query structuring techniques

These studies were all performed under the assumption that the coarse-grain ranking formula used for TREC was weaker

than the fine-grain ranking formula. The concern was that coarse-grain ranking did not retrieve a large enough

percentage of relevant documents in the initial retrieval set.

It was thought that once these documents were retrieved, the fine-grain algorithms would effectively use proximity and

term frequency information to sort the documents and put

all of the truly relevant ones at the top of the list.

Unlike other systems, ConQuest did not have funding for these TREC studies. This put the TREC studies in direct conflict with other more pressing concerns, such as supporting customers, or providing new functionality such

as client/server. As a result, the testing from these early studies proved

ambiguous and unreliable. We believe that this was due to the following:

• Since time and resources were limited, tests were

performed on only a small number of queries (5-10).

This did not provide a large enough sample set of

queries to produce reliable test results.

• ConQuest never tested the original assumption that coarse-grain was the limiting step in improving

accuracy.

• The queries for this testing were taken from the TREC-1 final test queries. However, many of these

queries were hastily constructed and thus added noise

to the test results.

Just before the TREC-2 results were due, ConQuest decided

to concentrate most of its effort on improving the tools used to generate queries. The tools and processes created are described in the next section.

Generating Queries for TREC-2

Generating queries was primarily an automatic process, based on the initial TREC-2 topic descriptions. Manual

input was used primarily to remove things: Words, word

meanings, and expansions. This produced queries with only

the terms that are relevant. If needed, a user can also set weights for query terms.

Note that all manual steps were performed for all queries before any documents were retrieved. In other words, no

feedback information was used in generating the queries.

This makes ConQuest fully compliant with the rules for ad- hoc queries in TREC-2.

Automatic Query Generation Steps

A special program was created to convert TREC-2 topic

descriptions into ConQuest query log files. The architecture of this program is show in Figure 3.

267
TREC-2 Topic

_ Descriptions Parse Topic SGML Codes Tokenize

Morphology

and

Idiom Processing H

Remove

Stop Words

Remove other TREC-2

Function Words Expand All Word Mesmings

ConQuest Query Logs

Figure 3 Program to Automatically Generate Query

Log Files The modules in the program are as follows: Parse Topic - Reads through the topic looking for the SGML codes (such as <description>). The location within the topic for all words in the query are preserved

in the final query log files. Tokenize - Divides up strings into tokens. Morphology - Locates all words in the dictionary and

reduces them to root words if possible.

Idiom Processing - Collects idioms together as single

terms, such as "United States." Remove Stop Words - Removes conjunctions,

determiners, auxiliary verbs, prepositions, etc. Remove Function Words - Removes words such as "document," "relevant," and "retrieve" which are used

often in TREC-2 narratives but do not help retrieval. Expand Word Meanings - All word meanings are expanded using the ConQuest semantic network and all expansions are added to the query. Note that all of these steps occur automatically with no manual input. The program also generates other statistics, such as the count of each term in the query, a count for each term for each section of the query (sections being the topic,

description, narrative, concepts, and factors), and the total number of words in the query.

Manual Query Generation Steps

There were two manual steps used to generate queries:

1 . Remove words, word meanings, and/or expansions

2. Set term weights (if necessary)

Fortunately, ConQuest has graphical user interfaces (GUIs)

for removing words, word meanings, and expansions from

the queries automatically generated. A user merely brings

up the query and uses the mouse to select items to be

deleted.

In TREC-2, terms were not weighted in the traditional sense, but rather were categorized into three sets:

1 . Terms that embody the entire query, which would

make good search terms if used by themselves

2. Terms which embody a necessary portion of the query, but not the entire concept

3. All other related terms

These categories provide simple guidelines for setting term

weights, which make it much easier to generate queries.

Evaluations using the TREC-2 test topics determined the functions for the actual term weights.

To emphasize once more, no document feedback was used

for these manual steps. All query adjustments were

performed without executing any query. Only after all queries were generated were the final results generated.

The TREC-2 Results

ConQuest scored very well in TREC-2. In particular, our

recall percentages were quite high. Our average precision scores were not as good, but still competitive.

ConQuest submitted two sets of results for TREC-2,

CnQstl and CnQst2. Both sets used the same coarse-grain

algorithm which retrieved the best 5000 documents from

the database. The difference between the two results was how these 5000 documents were sorted to derive the top

1000 documents which were used for the official results. The first set (CnQstl) used fine-grain as the only sorting

algorithm. This algorithm primarily depends on local proximity information, although word statistics and query

structure are also incorporated.

The second set of results (CnQst2) was a weighted average

of the fine-grain and coarse-grain statistics for each

document. As it turned out, this combination of local (finegrain) and global (coarse-grain) statistics provided

significantly better statistics. The relatively modest addition of global information

improved the results more than expected. Previous

experience had always indicated that fine-grain information,

especially the proximity test, was the strongest contributor

to document relevancy.

Some additional insights can be extracted from topic

analyses presented at the TREC-2 conference. Specifically, the topics where ConQuest excelled over other systems were also those which tended to have fewer relevant documents in the database. This indicates that local proximity statistics (used by ConQuest) are more important

for these queries, since most other systems in TREC-2 are heavily weighted towards global document statistics. In other words, ConQuest appears to perform better for queries

where one needs to find the "needle in the haystack."

Post TREC Analysis

After TREC-2, we had the chance to clean up our initial

tests, gather new statistics, and perform some additional

analysis.

The first step in this process was to prove the accuracy of

the coarse-grain algorithm. Remember that initial tests attempted to improve the coarse-grain algorithm. But did

the coarse-grain algorithm really need improvement? One

indication that coarse-grain was accurate was provided by

the CnQst2 run, which performed better than expected.

268
To check out the coarse-grain rank, we constructed graphs

which more clearly shows its performance. Since fine-grain can only work on the results of the coarse-grain algorithm,

what is the loss in recall for coarse-grain?

The following graph shows the cumulative recall percentage

as documents are retrieved from coarse-grain rank. Every

time a relevant document is retrieved, the recall percentage

gradually inches up towards 100%. Note: these tests were

run on just the Category B data. 100%

90%

„ 80%

g> 70%

60%

50%

I

40%

c

u

o

(0o 30%

20%

10% 4

0% H

I I I Documents Retrieved from

Coarse-Grain Rank

Figure 4 Cumulative Recall Percentage

for Query #110

Figure 4 shows two exciting discoveries. The first is that the coarse-grain performance achieves over 95% recall. This

strongly contradicts our initial fears that coarse-grain was

not retrieving enough relevant documents.

The second discovery is that the high recall figures are achieved quickly. This implies that ConQuest can retrieve fewer documents (greatly improving speed) and still achieve

high accuracy. To further establish these claims, we repeated the analysis on all queries in the TREC-2 topic set, then averaged the

results together, as shown in the next graph:

100% 95%

90%

85% 80%

75%

70%

65% 60%

55%

50%

t

Documents Retrieved by Coarse^rain Rank Figure 5 Cumulative Recall as Documents are Retrieved

using Coarse-Grain Rank

This figure is an average over all queries. The average

strongly correlates with the results from query #110. This

verifies the two discoveries identified above. Some initial studies also more clearly show the difference between fine-grain and coarse-grain sorting of documents.

The following figure shows both graphs superimposed:

90 T

c

n

>

« Hi = S

o E

(0 u

80 ••

70 -

60

50 •

g S

40

>

3

Eo

30

20

10

Coarse Grain Sorting

Fine Grain Sorting

Figure 6

1 1 1

Documents Retrieved from

Coarse-Grain Rank

Coarse-Grain Sorting vs. Fine-Grain Sorting

for TREC-2 Topic #135

In this diagram, we see that fine-grain sorting is in fact better than coarse-grain. In other queries, the results are more mixed. Clearly, the difference is not as great as was

initially assumed.

This suggests that the area where ConQuest can most

improve is not in the coarse-grain ranking algorithm, but

rather in improving the fine-grain algorithm, or providing a

better combination of the two. Upon further study, we believe we now know why. When

the fine-grain algorithm was developed, the programmers

assumed an average query length of about 5 words. Studies

of typical users indicate that their preferred query type is a

269
simple two or three word phrase. Therefore, a fine-grain

rank tuned for short queries has provided the best and most

accurate system for our commercial users.

In TREC-2, however, the queries are often 40-50 words

long. These analyses have shown us that our initial fine- grain algorithms are not as accurate for such long queries.

Future Analysis

Our tests indicate that more study of other fine-grain

algorithms is where ConQuest can most likely improve its scores for TREC-3. New ways of looking at proximity and

positional information in the document will be explored and

compared against the existing coarse-grain ranking results. We still feel that our existing fine-grain algorithm is best

for the typical commercial user, and we are looking for ways to fully test this hypothesis.

Finally, we are now much more sensitive to the effects of

query size on fine-grain algorithms and are looking more

closely at ways to desensitize our fine-grain algorithms, or

to adapt them easily to different query lengths.

270
Description of the PRC CEO Algorithm for TREC-2

Paul Thompson

PRC Inc., Mail Stop 5S3

1500 PRC Drive

McLean, VA 22102

Phone: 612/687-4650

E-mail: thompson@research.westlaw.com

(current affiliation: West Publishing Company)

Abstract

This paper describes an application of the

Combination of Expert Opinion technique to combine the results of multiple retrieval

methods used on the TREC-2 collection.

The methods being combined were weighted

by their TREC-1 performance.

1. Introduction

This paper describes work done on the

TREC-2 project at PRC Inc. in collaboration

with Professor Edward Fox and his

colleagues at Virginia Polytechnic Institute

and State University (VPI&SU). The reader

should refer to the description of their

system included in these working notes for

further details on the common processing of

the TREC-2 data shared by PRC and

VPI&SU (Fox et al. 1993). PRC used its algorithm, the Combination of Expert

Opinion (CEO), to combine the results of

VPI&SU's runs. VPI«feSU used a different

combination technique for their final results.

Originally the intent was that the CEO

algorithm would be integrated with the

SMART system used by VPI&SU. Both

upper and lower level combination of results

would take place, i.e., at the lower level of

individual document features within a

particular retrieval method and the upper

level of combination of the output of the

individual methods themselves, i.e., the

various cosine and p-norm methods used by

VPI&SU. For TREC-1 we were not able to

train the CEO algorithm, so that the

weighting of the various methods would be

optimized based on relevance judgments.

For TREC-2 we used the 11 point average

scores obtained by the various methods for

TREC-1 for weighting. Again we only used

the upper level of CEO. For TREC-1 we

found that combining all methods resulted in

lower performance than using the single best

method. This year our first version

combined the top two methods, based on TREC-1, while the second version used the

top five methods.

2. Combination of Expert Opinion

The statistical technique of CEO provides a

solution to the problem of combining

different probabilistic models of document

retrieval. This technique is expected to result in improved precision and recall over

that provided by any one model, or method,

since research has shown that various

retrieval models retrieve different sets of

more or less equally relevant documents

(Katzer et al. 1982, Fox et al. 1988). In the

271
Bayesian formulation of the CEO problem

(Lindley 1983) a decision maker is interested

in some parameter or event for which he/she

has a prior, or initial, distribution or

probability. The decision maker revises the

distribution upon consulting several experts,

each with his/her own distribution or probability for the parameter or event. To

effect this revision, the decision maker must

assess the relative expertise of the experts

and their interdependence, both with each

other and the decision maker. The experts'

distributions are considered as data by the

decision maker, which is used to update the

prior distribution.

For automatic document retrieval, the

retrieval system is the decision maker, and

different retrieval algorithms, or models, are

the experts (Thompson 1990a,b, 1991). This

is referred to as the upper level CEO. At

the lower level the probabilities of individual

features, e.g., terms, within a particular

retrieval model can be combined using CEO.

In lower level CEO the retrieval model is the decision maker and the term probabilities are viewed as lower level experts. The

probability distributions supplied by these

lower level experts can be updated,

according to Bayes theorem, by user relevance judgments for retrieved documents.

These same relevance judgments also give

the system a way to evaluate the

performance of each model, both in the

context of a single search of several

iterations and over all searches to date.

These results can be used in a statistically

sound way to weight the contributions of the

models in the combined probability

distribution used to rank the retrieved

documents. Since various algorithms, such

as p-norm, are expressed in terms of

correlations rather than probability

distributions, it was necessary to extend the

CEO algorithm to handle correlations. So

far this extension has been handled in a

heuristic fashion. If a retrieval method, e.g., one of the cosine methods, returned a value

between 0 and 1 as a retrieval status value;

the logistic transformation of this weight was

interpreted as an estimate of the mean of a

logistically transformed beta distribution

which was provided as evidence to the

decision maker. Since there was no basis

with which to assign a standard deviation to

this distribution, as called for by the CEO

methodology, an assumption was made that

all standard deviations were .4045, a value

corresponding to a standard deviation of .1 in terms of probabilities. The CEO code

was written in g++.

For TREC-1 we used the CEO algorithm to combine all of the VPI&SU retrieval

methods except for the Boolean, i.e., weighted and unweighted cosine and inner

product measures as well as p-norm

measures of 1.0, 1.5, and 2.0. For measures,

such as the inner product and some of the

p-norm results not giving a retrieval status value in the 0 to 1 range, the result was

mapped to this interval by scaling the

highest score of the method in question for a given topic to the highest score given by

one of the cosine measures. Default scores

half way between 0 and the lowest score achieved by a particular method were used

for documents not retrieved in the top 200 in

response to a given topic, since the actual score of these documents was unknown. For

TREC-2 we followed the same approach

except that only the results of methods with

better TREC- 1 performance were combined.

Our first version used Cosine.atn and

Cosine.nnn, the two best VPI&SU methods

from TREC-1, weighted by their

performance on TREC-1. The second

version used these two methods and the next

best three. Inner. atn. Inner.nnn, and Pnorm

1.0, also weighted by their TREC-1

performance (see VPI&SU report for details on these methods). Figures 1 and 2 show

272
our summary official TREC-2 results for

versions 1 and 2 respectively.

Queryid (Num): all prceol

Total number of documents over all queries

Retrieved: 50000

Relevant: 10785

Rel_ret: 3561

Interpolated Recall - Precision Averages:

at 0.00 0.4768

at 0.10 0.2613

at 0.20 0.1807

at 0.30 0.1202

at 0.40 0.0875

at 0.50 0.0504

at 0.60 0.0328

at 0.70 0.0169

at 0.80 0.0142

at 0.90 0.0077

at 1.00 0.0031

Average precision (non-interpolated) over all rel docs

0.0904

Precision:

At 5 docs: 0.2120

At 10 docs: 0.2480

At 15 docs: 0.2707

At 20 docs: 0.2760

At 30 docs: 0.2753

At 100 docs: 0.2418

At 200 docs: 0.1756

At 500 docs: 0.1086

At 1000 docs: 0.0712

R-Precision (precision after R (= num_rel for a query) docs retrieved):

Exact: 0.1703

Figure 1: Summary scores for PRC version

1 using two best experts

Queryid (Num): all prceol

Total number of documents over all queries

Retrieved: 50000

Relevant: 10785

ReLret: 3323

Interpolated Recall - Precision Averages:

at 0.00 0.5963

at 0.10 0.3270

at 0.20 0.2306

at 0.30 0.1430

at 0.40 0.0866

at 0.50 0.0425

at 0.60 0.0323

at 0.70 0.0246

at 0.80 0.0209

at 0.90 0.0131

at 1.00 0.0041

Average precision (non-interpolated) over all rel docs

0.1120

Precision:

At 5 docs: 0.4120

At 10 docs: 0.4000

At 15 docs: 0.3920

At 20 docs: 0.3740

At 30 docs: 0.3527

At 100 docs: 0.2722

At 200 docs: 0.1974

At 500 docs: 0.1090

At 1000 docs: 0.0665

R-Precision (precision after R (= num_rel for a query) docs retrieved):

Exact: 0.1809

Figure 2: Summary scores for PRC version

2 using five best experts

273
3. Conclusion

Although selecting the best individual

TREC-1 VPI&SU retrieval methods for

combination and weighting them by their

TREC-1 performance seemed to be a reasonable strategy which would yield better

retrieval results than unweighted

combination of all methods, in fact CEO

performance was worse on TREC-2. This

was due, in part, to changes made in the

individual methods which made methods that

had been best in TREC-1 less effective than

other methods for TREC-2. These results

suggest that selection and weighting of

methods for combination based on

performance of earlier versions of the

methods is unwarranted.

References

Fox, E.A.; Koushik, P.; Shaw, J.; Modlin,

R.; Rao, D. 1993. "Combining Evidence

from Multiple Searches" this proceedings.

Fox, E.A.; Nunn, G.L.; Lee, W.C. 1988.

"Coefficients for Combining Concept Classes

in a Collection" Proceedings of the 11th.

International Conference on Research and

Development in Information Retrieval, June

13-15, Grenoble, France, p. 291-307.

Katzer, J.; McGUl, M.J.; Tessier, J.A.; Frakes, W.; DasGupta, P. 1982. "A study of

the overlap among document

representations" Information Technology:

Research and Development, vol. 2, p. 261-274.

Lindley, D.V. 1983. "Reconciliation of

probability distributions" Operations

Research, v. 31, no. 5, p. 866-880.

Thompson, P. 1990a. "A Combination of

Expert Opinion Approach to Probabilistic

Information Retrieval, Part 1: The

Conceptual Model" Information Processing

and Management, vol. 26, no. 3, p. 371-382.

Thompson, P. 1990b. "A Combination of

Expert Opinion Approach to Probabilistic

Information Retrieval, Part 2: Mathematical

Treatment of CEO Model 3" Information

Processing and Management, vol. 26,

no. 3, p. 383-394.

Thompson, P. 1991. "Machine Learning in

the Combination of Expert Opinion

Approach to IR" In Birnbaum, L. and

CoUins, G. (eds.) Machine Learning:

Proceedings of the Eighth International

Workshop (ML91), San Mateo: Morgan

Kaufmann, p.270-274.

274
Report of Progress for TREC

II

Wiliiain Kelleher

Systems Environments Corporation

November 1, 1993

1.0 Introduction

Systems Environments is a commercial category B participant in TREC. For the past two years we have been devel- oping a software system starting on a DOS based PC and

recently moving to Windows NT on the same 486/25 PC.

Much development time has been diverted toward getting

around bugs and trying to fit the system on small (200 Mb

disks and within the memory constraints of the DOCS sys- tem). At this time the system is still under development.

2.0 System Architecture

FORMS (Feedback, Object-oriented Retrieval Methods) is an object oriented, concept based information retrieval system.

FORMS is concept based because it creates a profile of the

users information need and matches that profile to the document profiles. Profile can be stored in a library of concepts which is retained from one use of the system to

another. Relevance feedback is an essential ingredient in the design of FORMS although so far it has not been used

in TREC.

FORMS is designed and implemented using the object ori- ented approach. Object oriented methods were chosen to create a system architecture that can be installed in a vari- ety of different environments using different architectures. In its smallest implementation FORMS can function as a personal information system on a single PC, or it can be

installed as a organization wide system using client server approach. The basic objects and their relationships are shown in the following diagram and described in the fol- lowing sections.

2.1 SGML Documents

A document arrives and first must be translated into an SGML (Standard Generalized Markup Language) Document.

275
User Interface

2.2 Document Profiles

Elements are the things found in text documents that capture the meaning imbedded in the documents. The basic

element in text is obviously the words. But there are other elements that can be very helpful. Identifying certain kinds

of proper nouns can be crucial to determining the rele- vance of text to an information need, hnportant proper nouns might be persons, places, and some kinds of things,

e.g. companies.

There are two distinct types of elements used in FORMS,

simple and complex. A simple element consists of a character string, e.g. a word, and an element type, e. g. a noun

or a noun phrase. Complex elements are various combinations of simple elements. For example a boolean 'and' of

several simple elements occuning in the same sentence, paragraph or document would be a complex element.

In general simple elements are only used to create document profiles elements. A document profile element IS-A

simple element that includes the document id and the number of occurrences of Th. element in the document.

2.3 Indexes

After the document profiles are created, those elements

that occur in more than one document are stored in the ele- ment Index. An second index from the document ID to the

full text of the document is also created so that the user can retrieve a document for inspection.

3.0 User Interface

The user interface objects (grey area in the diagram) provide object to display documents, retrieve documents

using a natural language query as well as stored concepts,

and a window that displays results.

3.1 Document Display

The document display window permits a user to enter a document ID and the system will simply retrieve the document from one of the text files and display it on the screen. When a document is displayed, the user has the option to indicate that the document is or is not relevant to the current query (or concept). When an indication of relevance is given the system modifies the concept/query profile and

provides a new rank list of retrieve documents.

3.2 Query Window

The query window actually consists several parts. A natural language part for entering a query, and a query profile

part that displays the elements of the query/concept along

with various statistics about each element. Most important

Report of Progress for TREC II

276
Basic Assumptions

is the probability that a document is relevant if it contains

a particular element. Each element is treated as being inde- pendent of every other element.

3.3 Results Window

The results window contains the list of all documents with a

probability of relevance to the current query/concept. Documents

are ranked by probability of relevance. For each document, the elements found in the document are listed as well as any previous

relevance information. The document ID selected from the

results window can be used in the document window to examine

the document.

4.0 Basic Assumptions

The approach being taken by FORMS is based a number

of critical assumptions.

• Using the axioms of probabilities as a foundation for determining the relevance of documents to a query

• Elements besides words, such as phrases, can be found

to make a significant contribution to retrieval accuracy

• A number of different approaches to identifying rele- vant elements are worth pursuing. These include proper noun identification, part of speech tagging, noun phrase tagging, and as yet undetermined relations that can be extracted from natural language.

5.0 Progress to Date

Frankly, there has been little progress to date beyond basic system development. In both TREC I and II, we have per- formed routing queries with very poor results. One obvi- ous reason is that we have had to perform the analysis by

breaking the texts into small sections and doing even class B in sections.

6.0 Future Work

FORMS is designed to provide information on the effec- tiveness of different approaches.

• To examine different approaches to incorporating rele- vance feedback into evaluating relevance of other doc- uments.

• To examine whether queries can be analyzed and clas- sified so that the system can determine which approach

is most likely to be successful for a particular query or

concept.

• To examine whether certain kinds of elements (where

an element is a word, a phrase, a proper noun, a verb, a cooccurrence etc.) can be predetermined to be helpful

in certain types of queries.

7.0 Results & Conclusion

At this time no results are supported by work performed

with FORMS. However, it is worth emphasizing that the

results in TREC seem to support the view that there is no

specific approach that is going to revolutionize information retrieval. Rather, it seems that improvements are going to come from attention to details and fmdtng the

right element to use at the right time.

Report of Progress for TREC II

277
I
UCLA-Okapi at TREC-2: Query Expansion Experiments

Efthimis N. Efthimiadis* and Paul V. Biron

Graduate School of Library and Information Science

University of California at Los Angeles

1 Introduction

This is the first participation of the Graduate School of Li- brary and Information Science, University of California at Los Angeles in the TREC Conference. For TREC-2, Category B, UCLA used a version of the Okapi text retrieval system that was made available to UCLA by City University, London, UK. OKAPI has been described in TREC1 (Robertson, Walker, Hancock-Beaulieu, Gull Sz Lau,

1993a) as well as in this conference (Robertson, Walker,

Jones, Hancock-Beaulieu, & Gatford, 1994). Okapi is a simple set-oriented system based on a generalized probabilistic model with facilities for relevance feedback. In addition OKAPI supports a full range of deterministic Boolean

and quasi-Boolean operations.

1.1 Objectives

The main research objective of the UCLA participation

in TREC-2 was to investigate query expansion within the

framework as provided by Okapi. More specifically, the

objectives were to:

• use an enhanced version of the Go-See-List (GSL) and

evaluate its effect on retrieval performance.

• investigate the performance of query expansion with

and without relevance information by varying the

number of documents that are treated as relevant and

the number of terms that are included in the expansion.

• compare the performance of diff"erent ranking algorithms for the ranking of terms for term selection during query expansion.

• compare the effectiveness in retrieval of user assigned

relevance judgements against hypothetically assumed

relevance judgements based on the top X documents.

•To whom ail correspondence should be addressed. Graduate School of Library and Information Science, University of Cahfomia at Los Angeles, 405 Hilgaxd Avenue, Los Angeles,

CA 90024-1520, e-mail: iacxene@mvs.oac.ucla.edu

1.2 The Okapi version at UCLA and the

WSJ database

The Okapi system consists of a low level search engine or basic search system (BSS), a user interface for the manual search experiments and data conversion and inversion

utilities.

The UCLA hardware consisted of Sun SPARC-2 machine

with 32 MB of memory, and 1 GB of disk storage.

The Wall Street Journal (WSJ) database was used for both the routing and ad-hoc searches. Because of the lack of adequate disk space on the UCLA machine the database was indexed at City University by Stephen Walker and it was then transferred (FTP-ed) to UCLA.

For TREC-2 the Okapi databases were built by indexing mainly the DOCNO and TEXT fields of the records. Inverted indexes included complete within-document positional information, enabling term frequency and term prox- imity to be used. Okapi's typical index size overhead is around 80% of the textfile size. The elapsed time for inversion of the WSJ database was about 12 hours.

At this point it is worth noting of (a) the nature of the

WSJ records, and (b) a limitation of Okapi's due to indexing.

(a) The WSJ records consist of documents that do not have the same kind of structure found in bibliographic

databases, such as INSPEC or ERIC. The records contain

the full-text of stories and have varied length, mostly longer

than the length of an average abstract of a bibliographic

database. In addition, the language and the style is mostly

'journalistic' as opposed to 'scientific', i.e. less structured.

One important issue is that some WSJ records often contain short multi-story articles which are completely unrelated one from the other. This type of record is usually a compilation of a number of one- or two-paragraph long news stories. The stories share no content relation between

them, the only common feature is their co-existence in the same record. This has implications in retrieval effective- ness, especially when such records are included in the pool

279
of documents that provide terms for query expansion, be- cause of the noise introduced by the terms taken from the

irrelevant stories.

(b) This last issue relates to a limitation of Okapi. The

version of Okapi used at UCLA retrieves documents at the

record level only. Retrieval at the paragraph level, which

would have facilitated a better handling of some issues like the above, is not currently available.

2 The weighting functions

The weighting of search terms can be said to involve two

levels:

level 1: A weighting function is used to weight the terms

for the initial query as well as the terms for subsequent

search iterations of the same query or some modified

version of the query.

level 2: A weighting function is used for the weighting of

candidate terms for query expansion.

Sections 2.1 and 2.2 discuss functions used in level 1 and

level 2 respectively.

2.1 Search term weighting

When relevance information is not available the above

weight reduces to approximately the inverse document fre- quency (IDF).

For calculating the total weight of a document the fol- lowing function was used which is based on the binary independence model, and takes into consideration the 2-Poisson

model for within document frequency (tf) and the document length. These are described in detail in Robertson et I al (1993b). The purpose of the UCLA Okapi system was

'

to evaluate the existing Okapi models and therefore did not allow for modifications of the existing functions. For

compatibility purposes and for comparisons it was decided

to use the BM15 (best match) function for the runs. The

BM15 best match weigthing function is:

, . , V^/'/- \ \ I (avedl — dl) docweightbmih = > ((71—

-tt) X ti;/4 ) + A;2 X ng x -i ,, , ,'0

where k\ and are unknown constants. In the UCLAOkapi implementation the values for these constants are: kx — \ and ^2 = 1-

2.2 Query expansion term weighting

The ranking algorithms that were considered for the ranking of terms for query expansion were: wpq, emim, porter,

rJohi and rJiilo. These algorithms are described briefly

below.

The theory of relevance weights (Robertson & Sparck

Jones, 1976) provides the basic probabilistic model. The

binary independence or relevance weight model assigns a weight to each term and the matching function for each

document is given by the 'simple sum-of-weights' ovei all of the terms in the query.

The weight of a term is calculated by following function

which is also known as the f4 point-5 formula:

Wf4 = log

(r + 5)(7V -n-R + r + .5) (n-r + .5){R- r + .5) (1)

where,

N is the total number of documents in the collec- tion; R is the sample of relevant documents as defined

by the user's feedback; n is the number of documents indexed by term t; r is the number of relevant documents (from the

sample R) assigned to term t.

2.2.1 The wpq algorithm

This algorithm is based on an independence assumption

that holds between a query expansion term and the terms in the entire previous search formulation (Robertson, 1990).

According to the relevance weighting theory, the inclusion

of term t in the search formulation with weight Wt will increase the eff'ectiveness of retrieval by

wpq = wt{pt - qt) (3)

where, Wt is a weighting function, which in this case is the

Wf^; pt is the probability of term t occurring in a relevant

document; and qt is the probability of a term t occurring

in a non-relevant document.

This means that irrespective of the weighting function

(wt) used the rule for deciding the inclusion of a term in a

query expansion search should be based on the ranking of

wpq instead of Wt alone. Substituting the weighting function and the probability of relevance in wpq with r, R, n, N we get:

, {r + .5){N-n-R+r + .5) ,r n-r . "^^ = ^S (n-. + .5)(fi-. + .5) -(fl-ATTfl) 0

280
The wpq algorithm combines the effects of the relevance

weighting theory, as expressed by the Wf^ component,

which assign greater importance to the infrequent terms

with the frequency of occurrence of a term in the relevant

document set.

2.2.2 The emim algorithm

The expected mutual information measure {emim) is a term weighting model incorporating relevance information

in which it is assumed that index terms may not be dis- tributed independently of each other, (van Rijsbergen,

1977; Harper and van Rijsbergen, 1978; van Rijsbergen,

Harper & Porter, 1981)

The emim weight reduces to the f4 weight when the "de- gree of involvement", i.e. the joint probabilities, are all unity. Assuming the same definitions for n, N, r, R, as those already used earlier, the emim weight of a term is calculated as follows:

Eiq = Pnin - Pl2«12 - P21«21 + P22«22

, rN

{n-r)N

, {R-r)N

+ log

(N -n)R

(N - n- R + r)N

{N - n){N - R) {N -n- R + r)

• resolves ties according to their term frequency, n, from

low-to-high frequency.

It was hypothesized that the rJohi algorithm would have

an almost identical ranking to porter and a performance ap- proaching that of wpq and emim. More differences between

the algorithms may occur if the size of the set of relevant

documents (R) gets larger. Conclusions about the algorithm however could not be drawn before it was evaluated

against the other algorithms. The results of that evaluation

are reported in Efthimiadis (in press) where the rJohi al- gorithm demonstrated better performance when compared

to the other algorithms.

2.2.5 The r.hilo sort

A variant of the rJohi algorithm is to rank candidate terms

for query expansion using the r_hilo rank which:

• ranks terms according to r, i.e. their frequency of oc- currence in the relevant document set, and

• resolves ties according to their term frequency, n, from

high-to-low frequency.

Since the r^hilo algorithm will result in sorting terms in exactly the opposite way of the rJolii algorithm it was in- cluded as a control for the study.

2.2.3 The porter algorithm 3 Methodology

Porter and Galpin (1988) describe a ranking formula used

in the MUSCAT online catalogue:

porter R

n

(5)

where r, R, n, N are defined as in the f4 weight (eq. 1).

2.2.4 The rJohi algorithm

The rJohi algorithm has been proposed by Efthimiadis

(1993a) as the result of the observation of the ranking be- havior of six algorithms used for ranking terms for query

expansion.

The rJohi ranking algorithm:

• ranks terms according to r, i.e. their frequency of oc- currence in the relevant document set, in descending

order and

3.1 Runs

Initial tests were performed in topics 1-50 where the dependent variables were the weighting function and the query

processing of terms. ^From the results obtained it was es- tablished that the function to use will be BM15 and that

the parsing of the Topics would include both single terms

and "phrases" as defined by comma delimited text in the

Topics.

The table below (Table 3.1 gives all the variables used

in constructing the runs. The options available for each

variable are also provided.

Weighting Function: Best match function EM15 (see

equation 2).

Phrases: Choice of YES, NO, or BOTH. This determines

the type of parsing of the "Concepts" and "Title" fields

281
Table 3.1 Methodology for the Routmg Runs on Topics 51-100 Query Number of No. of Docs

Weighting Expansion Terms used for UCLA

Function Phrases QE Algorithm Expanded Auto Rel Fbk GSL

bml5 no no 0 0 no

yes yes 67711771 10 5 yes both porter 20 10 rjohi 30 15

rjiilo 20

from the Topics, which were the source of the search terms. NO means that the terms extracted from the

Concepts and Title fields are single terms only. YES

means that phrases get extracted as determined by the

simple routine, where a phrase is identified by using

the punctuation found in the Concepts and Title fields. BOTH is the combination of the two methods and the

terms are searched as single terms as well as phrases.

Query Expginsion (QE): The choice of query expansion

algorithms is one of wpq, emim, porter, rJohi, rjiilo.

Terms expanded: This specifies the number of terms to include in the expansion. When the number of terms

expanded is zero, then only the initial query is run. Feedback documents: This defines the number of top

ranked documents to be treated as relevant and to provide the source for the terms for query expansion.

UCLA GSL: defines whether the standard Okapi GSL or the UCLA enhanced version of the GSL will be used.

Because of the many parameters involved in each run

the names of rims have been deliberately made explicit,

which however resulted in rather long names. For example, bml5.plib.qey:r_lohi-10-5.uclagsly means that for this rim the weighting function used was the BM15, phrases were set to BOTH, query expansion took place, the rJohi

algorithm was used for the ranking of terms for query ex- pansion, 10 terms were added in the expansion, 5 documents provided the source of the terms for the expansion,

ajid the UCLA enhanced GSL was also used.

3.2 Go-See-List

The Go-See-List (GSL) is a look-up table that contains

stopwords, semi-stopwords, prefixes, go-phrases and syn- onym classes. The GSL is used during the indexing of a database cis well as during searching.

Stopwords contain an array of terms that are thought

to contain no or little value for retrieval. These include,

contractions, prepositions, adverbs, etc.

The semi-stopwords are terms that are thought to have

low value for retrieval purposes. Therefore, a semistopword will be searched only during the initial search if

it has been part of the user's search statement. If, however,

the term has emerged as the result of a query expansion it

is stopped, i.e. excluded from the pool of candidate terms

for query expansion.

Go-phrases are mostly noun-phrases that need to be

searched as one word or else the precision will be very low,

e.g. New York. GSL contains a small number of selected go-phrases.

Synonym entries contain a mix of terms/concepts that

are treated as synonyms for retrieval purposes. These may

be true s^Tionyms, quasi-synonyms, or unrelated semantically terms which are grouped together because of some common properties which have value for retrieval. Finally,

the synonym entries also contain term variants that are known to "escape" from the conflation algorithm. The

structure of the UCLA GSL is given in the table below.

The Go-See-List (GSL)

City added UCLA

by UCLA total stopwords 411 72 483

semi-stopwords 58 58

prefixes 18 18 Go-phrases 43 84 127 Synonyms 359 604 963

For the UCLA GSL, the Titles and Concepts of Topics 1-100 were analyzed and synonym classes were gener- ated from the data. The list includes: 40 personal names,

and 250 synonym classes. In addition, a list of organizations and a list of common business acronyms and abbreviations was compiled.

3.3 Query term selection

Query terms were selected from the Title and Concepts

fields of the records. The processing of these fields was

very simple. Programs written in awk and perl were used

to isolate the required fields, which were then parsed and

the resulting terms stemmed in accordance with the in- dexing procedures followed for building the WSJ database.

282
This process resulted in one-word query terms. When ap- propriate the procedure also output phrases by treating the

punctuation available in these fields as the phrase delimeter.

Queries were then generated automatically from the Title and Concepts fields. Exactly the same queries were used

in the Routing and Ad hoc searches.

3.4 Term selection for query expansion

a) Routing searches: Query expansion in the routing

searches was performed through query modification

without relevance information. As indicated in the table, that describes the construction of the runs inthe

methodology section, the number of documents used

could range from the top 0-20 documents, in incre- ments of 5 documents. These top ranked documents

were treated as relevant and were analyzed in order

to provide terms for the expansion. Expansion terms were selected by pooling all the terms and then weighting these terms with one of the five ranking algorithms

as specified by the run. Then the top 10, 20 or 30 terms were added to the original query terms and searched.

b) Ad hoc searches: The term pool consisted of all the

terms of the documents judged as relevant. For the Ad

hoc searches with feedback of the official results, the

top 10 terms as determined by wpq were chosen for expansion and were searched together with the initial query terms.

c) Rules for term selection: The following rules were

followed for the inclusion or exclusion of a term during

selection for query expansion:

a) numbers were excluded as terms,

b) all terms whose frequency (n) is equal to the number of relevant documents seen {R), i.e., if n <= R, were excluded.

3.5 Search procedure

All searches. Routing and Ad hoc, were automatic and de- termined by the specifications made for each run. There

were no manual searches.

3.5.1 Ad hoc searches and searchers

There were no manual searches. For the Ad hoc searches

with relevance feedback, i.e. uclafl (official results), rel- evance assessments were provided by two searchers. The

odd numbered topics were assessed by one searcher and

the even numbered topics by the other.

3.5.2 Relevance assessments

During the Ad hoc searches, the guidelines for relevance

judgements were:

a) review the entire document, when judging relevance, even if it seems to be peripheral or not relevant. The

reason being that many of the articles were found to be collections of brief news stories, with the relevant

part of the text hidden in (the middle or the end of)

the text.

b) target for 10 relevant documents; stop as soon as 10 are found or at the 20th document. However, if 3 relevant

have not been found continue till 3 are found (this is because OKAFl will not do an expansion if it has less than 3 documents).

3.5.3 Ad hoc additional runs

Following the TREC conference, a set of runs was con- ducted on the Ad hoc queries in order to complete the eval- uation of the five ranking algorithms for query expansion

that were studied.

The relevance judgements made in the Ad hoc run uclafl (fdbk.bml5.phb.qey:wpq-10-10.uclagsly) were ex- tracted and used in the subsequent runs. The process fol- lowed in these additional runs is described below:

• Four new Ad hoc runs were done; one for each of the

remaining algorithms which were used for the ranking

of terms for query expansion, i.e., emim, porter, r^hilo,

rJohi.

• The same initial query, which was generated automatically, was used for all searches.

• The relevance judgements made in the initially re- trieved set of the official Ad hoc run were extracted

and then simulated in the additional runs.

• Query expansion terms were ranked using the algorithm that was designated by each run. The 10 top

ranked terms from the pool were added to the query.

3.6 Problems & Limitations

Lack of equipment has been a major problem in our participation. In order to participate in TREC, SUN Microsystems provided an equipment grant (SUN Sparc-2)

in March, however no disk was initially available, but a 1- Gigabyte disk was acquired in June. Consequently, only

the Ad hoc runs were included in the official results.

283
A limitation of the UCLA version of OKAPI is that it does not allow modifications of the basic retrieval functions

(i.e., the BMs or best match functions).

4 Results and Discussion

The results of the Routing runs, the Ad hoc runs and the

Ad hoc additional runs are given in Table 1, Table 2 and

Table 3 respectively.

Routing runs

The 35 Routing runs given in Table 1 are presented in descending

recall values. The runs bml5.ph[ynb] .qen.uclagslCyn]

,

i.e., the runs without query expansion, were used as baseline runs in order to facilitate comparisons. All other runs

reported in the table include query expansion.

The results indicate that runs with query expansion,

where the rJohi or the r-hilo algorithm was used performed

better than all other runs in terms of Recall, Average Precision, and R-Precision.

Ad hoc runs

>From the three oflBcial Ad hoc runs, uclaal, was the au- tomatic run that did not include query expansion and has

been used as a baseline-run, uclaa2, was an automatic run

that included query expansion without any relevance infor- mation, and uclaf 1, was a run with user supplied relevance

feedback and query expansion.

In terms of R-Precision and Average Precision the run

with feedback and query expansion (uclaf 1) did better

than the automatic run with query expansion (uclaa2),

but the baseline was slightly better.

Ad hoc additional runs

The results of the Ad hoc additional runs are given in Table 3. The official run with feedback (uclafl) using wpq

for the expansion is compared to the runs which used the

rJohi, r.hilo, emim and porter algorithms respectively for the expansion. The results indicate that rJohi and rJiilo have performed better than the other algorithms. These

results further corroborate the results obtained from the

routing runs.

In order to further validate the results the sign test as well as the t-test were performed on the data. The results from the sign test are given on Tables 4-15. The tables are arranged in sequence starting from Precision at 15, 30,

and 100 documents, Average Precision, Recall-Precision,

to Recall. In each case, two tables are given; the first ta- ble gives the differences and the second the probabilities.

As it can be expected there are no differences at Precision

at 5 documents and at Precision at 10 documents because

these were the same for all five runs. For this reason the

corresponding pairs of tables have not been included in the

paper. The results also show no significant differences at Precision at 15 documents and at 30 documents. Signifi- cant results appear at Precision at 100 documents where

rJohi « r-hilo > emim wpq « porter.

The sign test results on Average Precision demonstrate

that rJohi « r_hilo > wpq ^ emim » porter, where

emim > porter. The results on Recall show some grouping between the algorithms, so that rJohi w rjiilo >

emim ft: wpq > porter. The results from the Recall- Precision indicate that rJohi fa r_hilo emim > wpq fs porter with rJohi > emim but not significantly better and

with wpq slightly better than porter.

>From the study of the sign test results certain overall comments emerge about the performance of the five algorithms. The results seem to be consistent throughout with

rJohi performing better than the other algorithms. Dif- ferences between emim, wpq and porter are not consistent

but it seems that emim is slightly better than wpq which is better than porter.

To further strengthen the validity of the results the t- test was per formed on the data. The t-test results are given on Tables 16-21. The tables are arranged in sequence

from Precision at 15, 30 and 100 documents. Average Precision, Recall-Precision, to Recall. Each table gives the

Mean difference, the standard deviation difference, the t- statistic and the probability. As in the case with the sign

test there were no differences for Precision at 5 documents

and Precision at 10 documents and therefore the corre- sponding tables have not been included in the paper. Similarly, there are no significant differences at Precision at 15 documents and Precision at 30 documents. The results at Precision at 100 documents show that rJohi m rJiHo >

emim « wpq m porter, this result is the same as the

sign test. The results from Average Precision demonstrate

that rJohi w r.hilo > emim w wpq w porter, with

emim better than porter. For Recall the results are that

rJohi « r.hilo > emim ss wpq > porter. Finally,

the Recall-Precision results demonstrate that rJohi «

r.hilo w emim > wpq > porter, where r^hilo is better than emim.

The results of the t-tests are consistent for the algorithms

284
and corroborate the results obtained from the sign tests. The two tests indicate that rjohi and r_hilo have performed

consistently better than the other algorithms.

5 Conclusions

• The results obtained from the use of the standard and

enhanced versions of the GSL indicate that further re- search is needed in order to determine the effectiveness of the GSL-synonym list in retrieval.

• The combination of adding 10 terms from the 5 or 10

top ranked documents contributed to better retrieval performance.

The other term/document combinations, i.e. adding

20, 30, or 40 terms from 15 or 20 documents, etc., had

a negative effect on retrieval performance.

• The results from the routing searches indicate that

query expansion (i.e., feedback searches without rel- evance information, where X number of terms is ex- tracted from Y number of top ranked documents that

are treated as relevant to the query) improved retrieval performance depending on the algorithm used.

• The rJohi algorithm (Efthimiadis, 1993a) improved

retrieval performance in the routing runs when compared to the initial (baseline) search which did not

involve either a feedback search or query expansion.

• In the Ad hoc searches the results of the evaluation

of the five ranking algorithms indicate that rJohi per- formed better than the other algorithms. These results were further validated by the results obtained from the

sign test and the t-test.

• Although query expansion seems to work, the retrieval performance achieved was less than expected.

There are many reasons that account for these results and which are briefly addressed below.

1. Completeness of the TREC Queries:

The major factor that is being attributed to these

results is that the queries, i.e. TREC Topic Descriptions, are almost complete, i.e. contain all the important words required for the search. Query expansion is the process of supplementing

the original query terms and is particularly effec- tive when incomplete queries are available. Query expansion on these rather complete queries

seemed to have contributed to a small or even

a detrimental effect in overall retrieval perfor- mance.

2. Size of the TREC collection: The large size of the TREC collection raises the

issue of scalability and effectiveness of retrieval algorithms. The TREC collection is very differ- ent from that of the standard IR test collections,

such as ADI, Cranfield, CACM, NPL. TREC is 1-4 Gigabytes of text whereas the other collec- tions are smallish in size, i.e., only a few (1-50)

Megabytes. The behavior and effectiveness of al- gorithms in information retrieval has been studied in small collections and TREC provides the

challenge of scalability.

3. Nature of documents:

The documents in the WSJ database are mostly

long documents; full-text as opposed to short

bibliographic records; less structure when compared to bibliographic records; and with lan- guage and presentation less structured (journalistic style compared to scientific style);

4. Length of documents:

The records are long and often contain short

multi-story, usually unrelated, items.

When such documents contain relevant information for a topic, i.e., when one of the stories is relevant but all the others are not, these increase

noise and interfere with the selection of terms for query expansion. This is because all the terms

of that document will be included in the pool of

the terms for query expansion and there may be

a number of terms from other stories in that document that will be ranked higher than the terms

from the relevant story.

This reinforces the need to be able to retrieve at a paragraph level rather than at a document level.

6 Future Research

• evaluate in detail the level of the effect of the GSLsynonym list in retrieval performance

• evaluate the different effect of a local versus a global

thesaurus for query expansion

• evaluate the effect of variable bias in query expansion

term weighting

• investigate the retrieval overlap between different approaches, and

• explore data fusion techniques for output integration

285
Table 1: Runs with and without query expansion for topics 51-100.

(Runs are presented in descending 'Recall' values.)

R,uii_ ricimc r\. V g IX \^\^ i. 1 v> V,

1 Cj Prerfl Ol Prec[l5] Free [30] Prec[lOO] R-Prec Recall

bml5.phb.qey :rJohi-10-5.uclagsln 0.5640 0.5160 0.4893 0.4400 0.3288 0.3465 0.7322

bml5.phb.qey ;rJohi- 10-10. uclagsln 0 2960 0 5640 0 5160 0.4907 0 4400 0.3288 0.3472 0.7320

bml5.phb.qey:rjohi-10-15.uclagsln 0.2961 0, 5680 0.6160 0. 4907 U.O.<£Oo 0.34 72 A 7'30A

bml5.phb.qey:rJohi-10-10.uclag3ly u. Jybu 0. 5640 0 . 5 160 0. 490 7 n Q D U . o JoB 0 .3472 n 7 Q O A

bml5.phb.qey:r_hilo-10-10.uclagslr) U.2odU 0.5480 u. 4yuu 0.4733 0-4167 0.3148 0.3327 n TOO "5 U. I Zoo

bml5.phb.qey:r_hilo-10-10.uclagsly 0.2860 0.5480 u. 4yuu 0 - 4 733 0.4167 0.3148

bml5.phn.qen.uclagsly 0.2849 0.5560 0 .5040 0-4720 0. 4200 0.3164 n Q "3 T o U .00^0 0.7264 bml 5. phn.qen. uclagsln 0.2849 0.5560 0. 5040 0.4720 0.4200 0.3164 U.OOi o 0. 7264

bmlS.phb.qen.uclagsly 0.2846 0.5560 n t^n^n u . au^u 0. 3325

bml5.phb.qey:emi-10-10.uclag3ly 0.2746 0. 5240 0.4960 0.4573 0.4027 u. .^yoz 0.3106 0.7113 bml 5. phb.qey:emi- 10- 10. uclagsln 0.2746 0.5240 0. 4960 0.4573 0.4027 0.3106 0.7113

bml5.phb.qey:rJohi- 20-5. uclagsln 0.2968 0.5760 0.5240 0.5027 0.4527 0. Jod2 0.3508 0.7060 bm 15. phb.qey:rjohi-20- 15. uclagsln 0.2969 0.5800 0.5280 0. 5067 0.4520 0.3362 0 ."^512 0.7060 bm 15. phb.qey:rJohi- 20- 10. uclagsln 0.2967 0.5800 0.5260 0. 5067 0.4520 0.3364 0.3.^12 0. 7060

bml5.phy.qen.uclagsly 0.2406 0.4600 0.4680 0.4360 0.3800 0.2878 0.2967 0.6777 bml5.phb.qey:wpq-10- 10. uclagsln 0.2590 0.5360 0.4980 0.4440 0.3900 0.2884 0-301

7

0.6768 bml5.phb.qey:wpq-10-10.uclagsly 0.2590 0-5360 0.4980 0.4440 0.3900 0.2884 0.301

7

0.6768 bm 1 5. phb.qey:wpq- 10- 15. uclagsln 0.2570 0.5160 0.4780 0.4520 0.3953 n o o o 1 0.2963 0.6750 bm 1 5. phb.qey;rJohi- 30- 15. uclagsln u .zoyo 0. 5760 U. OOOO bml 5. phb.qey:rJohi-30- 10. uclagsln nu. Ofl ^oyuQn 0 . 5800 0 3370 U . DDoo bml5.phb.qey; r_lohi-30-5 . uclagsl n 0 2894 0 5840 \i . \JOO\J 0.5133 0.4520 0.3370 0.3450 0 6678 bml5.phb.qey: emi-20- 10. uclagsl y

0 2458 w - o u 0 4880 0.4547 0.3873 0.2696 0.2875 0.6599 bm 15. phb.qey:wpq-20-10. uclagsln 0 2443 0 5120 0 4660 0.4413 0.3847 0.2706 0.2838 0.6437 bml5.phb.qey;wpq-10-5 .uclagsln 0 2438 0 5440 0-4900 0.4533 0.3800 0.2740 0.2849 0.6423 bml5.phb.qey:wpq-10-5 . uclagsly 0 2438 0 5440 0 4900 0 4533 0 2740 0 2849 0 6423 bml5.phb.qey :por- 10-10. uclagsly 0 4980 0 4560 0 4047 nU oflfio . ZoO z u . zy oy 0 6362 bml5.phb.qey:por-l 0-10. uclagsln 0.2509 0.5320 n ORfiO u. zooz u. zyoy U.DoOZ bml5.phb.qey:emi-30- 10. uclagsly 0 2377 0.4860 0.4480 0 3780 U . jSDDD 0 2809 0 .633

1

bm 1 5. phb.qey:por- 20- 10. uclagsly 0.2558 0.5320 0, 5080 0.4 760 0.4113 0.2856 A O A O A n c o o bm 1 5. phb.qey:wpq- 20- 15. uclagsln u . ^y u . zoy

u

U.DZOo bm 15. phb.qey:por-30- 10. uclagsly 0.2494 0.5000 0.5080 0.4733 0.4167 0.2926 0.3088 0.6270 bml 5.phb.qey:wpq-30- 10. uclagsln 0.2318 0.5280 0.4920 0.4387 0.3753 0.2620 0.2744 0.6121 bm1 5. phb.qey:wpq- 30- 15. uclagsln 0.2271 0.5040 0.4860 0.4467 0.3700 0.2646 0.2668 0.6079 bm1 5. phb.qey:wpq-20- 5. uclagsln 0.2266 0.5360 0.4820 0.4467 0.3707 0.2570 0.2699 0.6016 bml5.phb.qey:wpq-30-5.uclagsln 0.2173 0.5280 0.4680 0.4347 0.3660 0.2480 0.2651 0.5790

Table 2: Ad hoc results: Runs with and without query expansion for topics 101-150.

(Runs are presented in descending 'Recall' values.)

Run. name Avg Prec Prec [5] Prec [10] Prec[l5] Prec [30] Prec [100] R-Prec Recall uclaal : auto.bmlS.phb.qen.uclagsly

uclaa2: auto.bml5.phb.qey:wpq-10-10.uclagsly

uclafl : fdbk. bml5 .phb . qey :wpq- 1 0- 1 0. uclagsly

0.3345 0.2957

0.3090

0.5840

0.5440

0.5880

0.5380

0.5180

0.5220

0.4973

0.4920

0.4893

0.4333

0.4207

0.4360

0.3098 0.2760

0.2884

0.3629

0.3289

0.3459

0.8155 0.7786

0.7745

Table 3: Performace Averages over all Topics of the Ad hoc Runs with Query Expansion.

(Runs Named after the Algorithm used in the Expansion.)

RunJMame Avg Prec Prec[5] Prec[10] Prec[15] Prec[30] Prec[100] R-Prec Recall rJohi 0.3414 0.5880 0.5240 0.4947 0.4427 0.3152 0.3688 0.8290

rJiUo 0.3388 0.5880 0.5240 0.4960 0.4347 0.3160 0.3692 0.8333 emim 0.3176 0.5880 0.5240 0.4920 0.4433 0.2938 0.3554 0.7989

uclafl: wpq 0.3087 0.5880 0.5240 0.4893 0.4360 0.2882 0.3460 0.7753

porter 0.2990 0.5880 0.5240 0.4893 0.4280 0.2798 0.3323 0.7457

286
Table 4: Sign Test Differences,

Precision at 1 5 Docviments wpq porter emim rjohx r.hilo 0 3 1 3 2 porter 4 0 4 3 1 emim 2 4 0 3 4 rJohi 5 6 4 0 3

T.hilo 5 5 5 3 0

Table 5: Sign Test Probabilities,

Precision at 15 Documents

wpq porter emtm rJohi T.hilo wpq 1.0000

porter 1.0000 1.0000 emim 1.0000 1.0000 1.0000

r.lohi 0.7266 0.5078 1.0000 1.0000

rJiUo 0.4531 0.2188 1.0000 1.0000 1.0000

Table 6: Sign Test Differences,

Precision at 30 Documents

wpq porter emim rJohi r.hilo wpq 0 18 12 13 15

porter 12 0 15 11 12 emim 14 18 0 16 19

r.lohi 18 18 18 0 15

r^hilo 13 18 16 7 0

Table 7: Sign Test Probabilities,

Precision at 30 Docimients wpq porter emim r-lohi r-hilo wpq 1.0000

porter 0.3613 1.0000 emim 0.8445 0.7277 1.0000 rJohi 0.4725 0.2652 0.8638 1.0000

rJiilo 0.8501 0.3613 0.7353 0.1338 1.0000

Table 8: Sign Test Differences,

Precision at 100 Docimients wpq porter emim T-lohi T.hilo wpq 0 22 8 8 8

porter 15 0 15 8 8 emim 19 24 0 12 9 rJoki 32 31 27 0 15

rJiilo 32 33 27 14 0

Table 9: Sign Test Probabilities,

Precision at 100 Documents

wpq porter emim T-lohi rjiilo wpq 1.0000

porter 0.3239 1.0000 emtm 0.0543 0.2002 1.0000 rJohi 0.0003 0.0004 0.0250 1.0000

T.hilo 0.0003 0.0002 0.0046 1.0000 1.0000

Table 10: Sign Test Differences,

Average Precision wpq porter emim T-lohi T-hilo wpq 0 29 15 10 11 porter 20 0 17 9 10 emim 25 32 0 13 16

T-lohi 39 40 36 0 31

T-hilo 38 39 33 17 0

Table 11: Sign Tea/ Probabilities,

Average Precision wpq porter emim T-lohi T-hilo wpq 1.0000

porter 0.2531 1 .0000 emim 0.1547 0.0455 1.0000

T-lohi 0.0001 0.0000 0.0017 1.0000

T-hilo 0.0002 0.0001 0.0223 0.0606 1.0000

Table 12: Sign Test Differences,

Recall wpq porter CTniTn T-loIll T-hilo wpq 0 24 8 10 7 porter 8 0 10 4 3 emim 15 25 0 9 6

T-lohi 27 28 25 0 14

T-hilo 29 32 26 14 0

Table 13: Sign Test Probabilities,

Recall wpq porter emim T-lohi T-hilo wpq 1.0000

porter 0.0080 1.0000 emim 0.2100 0.0180 1.0000

T-lohi 0.0085 0.0000 0.0101 1.0000

T-hilo 0.0005 0.0000 0.0008 1.0000 1.0000

Table 14: Sign Test Differences,

RecaU/Precision wpq porter emim T-lohi T-hilo wpq 0 19 5 10 8

porter 11 0 7 6 7 emim 17 23 0 11 11

T-lohi 23 26 19 0 13

T-hilo 27 26 20 12 0

Table 15: Sign Test Probabilities,

Recall/Precision wpq porter emim r-lohi T-hilo wpq 1.0000

porter 0.2012 1.0000 emim 0.0169 0.0062 1.0000

T-lohi 0.0367 0.0008 0.2012 1.0000

T-hilo 0.0023 0.0017 0.1508 1.0000 1.0000

287
Table 16: t-test, Precision at 15 Dociiments

iviccui SD

Runs Difference Difference t Probability

It* n 1 1 f\ 1 v 1 n h 1 /_/ttiL'/ 1 III t 0.0013 0.0285 0.3305 0.7424

?*_ A% lo j € TTX % 771 0.0040 0.0367 0.7712 0.4443

T loJllf CTil/tTTl 0.0027 0.0300 0.6282 0.5328

r.hilo/ porter 0.0067 0.0278 1.6973 0.0960

r^loki/ porter 0.0053 0.0325 1.1579 0.2525 emim/porter 0.0027 0.0380 0.4957 0.6224

rjiilo/wpq 0.0067 0.0337 1.3999 0.1678

rJoki/ wpq 0.0053 0.0352 J. .u / Uo U.Zoy / emim/ wpq 0.0027 0.0232 0.8137 0.4197

porter/ wpq 0.0000 0.0404 0.0007 0.9994

Table 17: t-test, Precision at 30 DocT-ments Mean SD

riuns Difference Difference t r ro DaDility

r^tiiio 1 r^lorii -0.0080 0.0298 -1.8984 U.UDOO

r^hiloj emim -0.0087 0.0583 -1.0521 n 0Q7Q

1 ^lU ILl / c lltl lit -0.0007 0.0585 -0.0810 0.9358

r.hilo/ porter 0.0067 0.0539 0.8748 0.3860

rdohi/ porter 0.0147 0.0518 2.0019 0.0508 emim/ porter 0.0153 0.0694 1.5624 0.1246

r-hiloj wpq -0.0013 0.0534 -0.1770 0.8602

rJohi/ wpq 0.0067 0.0530 0.8882 0.3788 emim/ wpq 0.0073 0.0458 1.1312 0.2635

porter/ wpq -0.0080 0.0450 -1.2590 0.2140

Table 20: t-test, Recall Mean SD

Runs Difference Difference t Probability

r.hilo/ T.lohi 0.0005 0.0312 0.1052 0.9166

r^hilo / emim 0.0363 0.0743 3.4503 0.0012

r.lohi/ emim 0.0358 0.0819 3.0935 0.0033

r-hilo/ porter 0.0731 0.1094 4.7267 0.0000

r.lohi/ porter 0.0727 0.1108 4.6356 0.0000 emim/ porter 0.0369 0.0965 2.7010 0.0095

r.hilo/ wpq 0.0506 0.0835 4.2805 0.0001

rJohi/ wpq 0.0501 0.0908 3.9007 0.0003 emim/ wpq 0.0143 0.0529 1.9106 0.0619

porter/ wpq -0.0226 0.0739 -2.1583 0.0358

Table 18: t-test. Precision at 100 Documents Mean SD

Runs Difference Difference t Probability

r^hilo/ rJoki 0.0008 0.0267 0.2118 0.8332

T.hilo/ emim 0.0222 0.0435 3.6060 0.0007

rJohi/ emim 0.0214 0.0469 3.2291 0.0022

r.hilo / porter 0.0362 0.0656 3.8991 0.0003

r-lohi/ porter 0.0354 0.0648 3.8658 0.0003 emim/porter 0.0140 0.0535 1.8494 0.0704

r.hilo/ wpq 0.0278 0.0557 3.5311 0.0009

rJohi/ wpq 0.0270 0.0600 3.1815 0.0025 emim/ wpq 0.0056 0.0301 1.3150 0.1946

porter/ wpq -0.0084 0.0410 -1.4496 0.1536

Table 19: t-test, Average Precision Mean SD

Runs Difference Difference t Probability

r-hilo / rJohi -0.0026 0.0153 -1.1935 0.2384

r-hilo/ emim 0.0212 0.0421 3.5637 0.0008

r.lohi/ emim 0.0238 0.0470 3.5786 0.0008

r.hilo/ porter 0.0398 0.0615 4.5727 0.0000

r.lohi/ porter 0.0424 0.0658 4.5547 0.0000 emim/ porter 0.0186 0.0592 2.2172 0.0313

rJiilo / wpq 0.0301 0.0537 3.9615 0.0002

rJohi/ wpq 0.0327 0.0603 3.8291 0.0004 emim/ wpq 0.0089 0.0335 1.8726 0.0671

porter/ wpq -0.0097 0.0358 -1.9107 0.0619

Table 21: t-test, Recall/Precision

Mean SD

Runs Difference Difference t Probability

r_hHo/ rJohi 0.0003 0.0246 0.0978 0.9225

r_hilo/ emim 0.0138 0.0450 2.1635 0.0354

rJohi/ emim 0.0134 0.0517 1.8396 0.0719

r.hilo/ porter 0.0369 0.0636 4.1048 0.0002

rJohi/ porter 0.0366 0.0669 3.8626 0.0003 emim/ porter 0.0231 0.0576 2.8398 0.0066

r.hilo/ wpq 0.0231 0.0532 3.0729 0.0035

T.lohi/ wpq 0.0228 0.0635 2.5401 0.0143 emim/ wpq 0.0094 0.0313 2.1135 0.0397

porter/ wpq -0.0138 0.0441 -2.2100 0.0318

288
Acknowledgements

Thanks to Stephen Robertson and Stephen Walker for making

OKAPI available to UCLA.

We are especially thankful to Stephen Walker for all his continuing help over and above that indicated in the text. We wish to thank Donna Harman of NIST for her support

during TREC-2.

This research was supported by a UCLA Academic Senate

grant that also provided financial support for the graduate re- search assistantship of PVB.

Finally, ENE is grateful to SUN Microsystems Inc. for the equipment grant of the SPARC 2 that made this research possible.

References

Efthimiadis, E.N. (1993a) A user-centered evaluation of ranking algorithms for interactive query expansion. In: Korfhage

R., Rasmussen E. and WUlett P. (eds.) Proceedings of the 16th International Conference of the Association of Computing Machinery, Specialist Interest Group in Information Retrieval, June 1993. Pittsburgh, PA: ACM Press, pp. 146-159.

Efthimiadis, E.N. (in press) 'User choices: As the yardstick for the evaluation of ranking algorithms for interactive query ex- pansion.' Information Processing & Management. In press.

Efthimiadis, E.N. 'Interactive Query Expansion: A User- based evaluation in a Relevance Feedback Environment.'

Majiuscript submitted for publication.

Harper, D.J. & van Rijsbergen, C.J. (1978) An evaluation

of feedback in document retrieval using co-occurrence data. Journal of Documentation, 5.^(3), 189-216.

Porter, M.F. & Galpin, V. (1988) Relevance feedback in a pubUc

access catalogue for a research library: Muscat at the Scott Polar Research Institute. Program, 22(1), 1-20.

Robertson, S.E. (1990) On term selection for query expansion.

Journal of Documentation, ^6(4), 359-364. Robertson, S.E. and Sparck Jones, K. (1976) Relevance weighting of search terms. Journal of the American Society for In- formation Science, 27(3), 129-146. Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A. &: Lau, M. (1993a) Okapi at TREC. In: Harman, D. K. (Ed.)

The First Text Retrieval Conference (TREC-1). Proceedings.

NIST Special PubUcation 500-207. Gaithersburg, MD: NIST,

1993. pp21-30.

Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.,

& Gatford, M. (1993b) Okapi at TREC-2. In: Harman, D.K.

(Ed.) Text Retrieval Conference (TREC-2). Gaithersburg,

MD: NIST, August 30-September 1, 1993, Proceedings, to appear.

van Rijsbergen, C.J. (1977) A theoretical basis for the use of co-occurrence data in information retrieval. Journal of Documentation, 33, 106-119. van Rijsbergen, C.J., Harper, D.J. & Porter, M.F. (1981) The

selection of good search terms. Information Processing and

Management, 17(2), 77-91.

A Runs

The runs presented here are grouped by algorithm used for the expansion. The list starts with runs that did not include query

expansion.

bml5.phn.qen.uclagsln

bml5 .phn, qen .uclagsly

bml5 .phy. qen. uclagsly

bml5 .phb..aen .uclagsly

bml5 .phb, aev : wpq-10-5.uclagsln

bml5 .phb.,qey;: wpq- 1 0- 5 . uclagsly

bml5 .phb,.qey;:wpq-10-10.uclagsln

bml5 .phb. : wpq- 10-10 .uclagsly

bml5 .phb..qey:: wpq- 10-15. uclagsln

bml5 .phb..qey::wpq-20-5.uclagsln

bml5 .phb..aev:: wpq- 20- 1 0 . uclagsln

bml5 .phb. aev ;wpq-20- 15. uclagsln

bml5 • phb. aev::wpq-30-5. uclagsln

bml5 .phb. H J

: wpq-30- 1 0 . uclagsln

bml5 .phb. aev: wpq-30- 15. uclagsln

bml5 .phb..aev:: emim- 1 0- 1 0 . uclagsln

bml5 .phb. aev:: emim- 10-10. uclagsly

bml5 .phb.,qey::emim-20-10. uclagsly

bml5 .phb.,qey:: emim-30- 1 0 . uclagsly

bml5 .phb. qey:: port- 10-1 0 .uclagsln

bml5 .phb. qey:: port- 10-10. uclagsly

bml5..phb.qey::port-20- 10. uclagsly

bml5 .phb. qey::port-30-10. uclagsly

bml5 .phb. qey::r_hilo-10-10. uclagsln

bml5 .phb. qey::r_hilo- 10-10. uclagsly

bml5 .phb. qey:: rJohi- 10-5. uclagsln

bml5 .phb. qey::rJohi-10-10. uclagsln

bml5,.phb. qey:: rJohi- 1 0- 1 0 . ucl agsly

bml5..phb. qey:: rJohi- 10-15 .uclagsln

bml5..phb. qey:: r_lohi-20- 5 . uclagsln

bml5,.phb. qey::rJohi-20-10. uclagsln

bml5,.phb. qey:: r-lohi- 2 0- 1 5 . ucl agsln

bml5, phb. qey::r_lohi-30-5. uclagsln

bml5,.phb. qey:; r_lohi-30- 1 0 . ucl agsln

bml5..phb. qey:: rJohi- 30- 1 5 . uclagsln

289

Incorporating Semantics Within a Connectionist Model

and a Vector Processing Model

Richard Boyd, James Driscoll, Inien Syu

Department of Computer Science

University of Central Florida

Orlando, Florida 32816

(407) 823-2341

FAX: (407)823-5419

e-mail: driscoll@cs.ucf.edu

Abstract

Semantic information obtained from the public domain

1911 version of Roget's Thesaurus is combined with keywords to measure similarity between natural language topics

and documents. Two approaches are explored. In one

approach, a combination of keyword relevance and semantic

relevance is achieved by using the vector processing model

for calculating similarity, but extending the use of a keyword

weight by using individual weights for each of its meanings.

This approach is based on the database concept of semantic

modeling and the linguistic concept of thematic roles. It is applicable to both routing and archival retrieval The second

approach is especially suited for routing. It is based on an AI

connectionist model. In this approach, a probabilistic

inference network is modified using semantic information to achieve a competitive activation mechanism that can be used

for calculating similarity.

Keywords: vector processing model, semantic data model,

semantic lexicon, inference network, connectionist model.

1. Introduction

The experiments reported here use a relatively efficient method to detect the semantic representation of text. Our

original method is based on semantic modeling and is described in [4,17,19].

Semantic modeling was an object ofconsiderable database

research in the late 1970's and early 1980's. Abriefoverview

can be found in [3]. Essentially, the semantic modeling

approach identified concepts useful in talking informally

about the real world. These concepts included the two notions

of entities (objects in the real world) and relationships among

entities (actions in the real world). Both entities and rela- tionships have properties.

The properties of entities are often called attributes. There

are basic or surface level attributes for entities in the real

world. Examples ofsurface level entity attributes are General

Dimensions, Color, and Position. These properties are prevalent in natural language. For example, consider the phrase "large, black book on the table" which indicates the General Dimensions, Color, and Position of the book.

In linguistic research, the basic properties of relationships are discussed and called thematic roles. Thematic roles are

also referred to in the literature as participant roles, semantic

roles and case roles. Examples of thematic roles are Beneficiary and Time. Thematic roles are prevalent in natural language; they reveal how sentence phrases and clauses are semanticaUy related to the verbs in a sentence. For example,

consider the phrase "purchase for Mary on Wednesday"

which indicates who benefited from a purchase (Beneficiary)

and when a purchase occurred (Time).

A main goal of our research has been to detect thematic

information along with attribute information contained in natural language queries and documents. In order to use this additional information, the concept of text relevance needs

to be modified.

In [17,19] the major modifications included the addition of a lexicon with thematic and attribute information, and a modified computation of a vector processing similarity

coefficient. TTiat research concerned a Question/Answer

environment where queries were the length ofa sentence and

documents were either a sentence or at most a paragraph. At

that time, our lexicon was based on 36 semantic categories,

and in that environment, our semantic approach produced a

significant improvement in retrieval performance.

However, for TREC-1 [4], document and topic length

presented a problem and caused our semantic approach based on 36 semantic categories to be of little value. However, as reported in [4], by breaking the TREC documents into paragraphs, a significant improvement was demonstrated.

This work has been supported in part by NASA KSC Cooperative Agreement NCC 10-003 Project 2, Florida High Technology and Industry Council Grants 4940-11-28-721 and 4940-11-28-728.

291
In Section 2, we describe our original semantic lexicon

and an extension which uses a larger number of semantic

categories. Section 3 presents an application of an AI

connectionist model to the task of routing. Section 4 presents an approach different than reported in TREC-1 [4], using our extended semantic lexicon within the vector processing

model. Section 5 summarizes our researdi effort.

2. The Semantic Lexicon

Our semantic approach uses a thesaurus as a source of

semantic categories (thematic and attribute information). For

example, Roget's Tliesaurus contains a hierarchy of word

classes to relate word senses [14]. In TREC-1 [4] and in earlier research [17,19], we selected several classes from this hierarchy to be used for semantic categories. We defined

thirty-six semantic categories as shown in Figure 1.

In order to explain the assignment of semantic categories

to a given term using Roget's Thesaurus, consider the brief index quotation for the term "vapor":

vapor

n. fog 404.2

fume 401

iUusion 519.1

spirit 4.3 steam 328.10

thing imagined 535.3

V. be bombastic 601.6

bluster 911.3

boast 910.6

exhale 310.23

talk nonsense 547.5

The eleven different meanings of the term "vapor" are given

in terms of a numerical category. We developed a mapping

of the numerical categories in Roget's Thesaurus to the thematic role and attribute categories given in Figure 1. In

this example, "fog" and "fume" correspond to the attribute State; "steam" maps to the attribute Temperature; and "ex- hale" is a trigger for the attribute Motion with Reference to Direction. The remaining seven meanings associated with

"vapor" do not trigger any thematic roles or attributes. Since

there are eleven meanings associated with "vapor," we

indicated in the lexicon a probability of 1/11 each time a

category is triggered. Hence, a probability of2/1 1 is assigned

to State, 1/11 to Temperature, and 1/11 to Motion with

Reference to Direction. This technique of calculating probabilities is being used as a simple alternative to a corpus

analysis.

It should be pointed out that we are still experimenting

with other ways of calculating probabilities. For example, as

in [8], a probabilistic part-of-speedi tagger could be used to further restrict the different meanings of a term, and existing

lexical sources could be used to obtain an ordering based on

frequency of use for the different meanings of a term.

As reported in [4], the use of36 semantic categories caused

problems when dealing with TREC documents. When the size of a document is large, a greater number of the 36

semantic categories are triggered in the document. Also,

when using the semantic approach described in [19] the probability present for each category in a document is often very close to one. Consequently, almost every one of the

j—

Thematic Role Categories

TACM Accompaniment

TAMT Amount

TBNF Beneficiary

TCSE Cause

TCND Condition

TCMP Comoarison

TCNV Conveyance

TDGR Deeree TDST Destination

TOUR Duration

TOOL Goal

TINS Instrument TSPL Location/Space

TMAN Manner

TMNS Means

TPUR Purpose

TRNG Ranee

TRRS Result TSRC Source TTIM Time

Figure 1. Thirty-Six

Attribute Categories

ACOL Color

AEID External and Internal Dimensions

AFRM Form

AGND Gender

AGDM General Dimensions

ALDM Linear Dimensions

AMFR Motion Conioined with Force AGMT Motion in General

AMDR Motion with Reference to Direction

AORD Order

APHP Physical Properties

APOS Position ASTE State ATMP Temperature

AUSE Use

AVAR Variation

Semantic Categories.

292
36 semantic categories becomes present in every document.

This causes semantic category weights to become very low

and useless within that approach.

As reported in [4], one way to solve this problem is to break TREC documents into paragraphs. But, another way

to solve the problem of long documents causing semantic

weights to be of little value is to have more semantic

categories. A large number of "semantic" categories can be

obtained (for example) by using aU the categories and/or

subcategories found in Roget's Thesaurus, instead of the 36

semantic categories we have used. This may be a deviation

from database semantic modeling. In any case, it needs to be

examined.

Consequently, for the experiments reported here, a semantic lexicon was created based on all the word senses found in the public domain 1911 version of Roget's Thesaurus. To provide an example, consider Topic 052 as shown

in Figure 2. Fi^re 3 indicates the keywords and frequency

information within Topic 052, along with the semantic

categories obtained from our extended lexicon for those keywords. Note that stemming was not used for the pro- cessing of Topic 052; so, some keywords in Topic 052 were

not located in our lexicon (e.g. sanctions).

The categories recorded in our extended semantic lexicon

usethe category numbers found in the 1911 version ofRoget's

Thesaurus. These numbers are then followed by a part-ofspeedi code also found in the 1911 version of Roget's

Thesaurus. The number after the part-of-speech code

represents a sub-category, but this number does not appear

in the 191 1 version of Roget's Thesaurus. That number was

created based on groupings of words within the thesaurus.

3. Connectionist Model Routing Experiments

Recent work suggests that significant improvements in retrieval performance will require a technique that, in some

sense, "understands" the content of documents and queries

and can be used to infer probable relationships between

documents and queries [2]. In this view, information retrieval

is an inference or evidential reasoning process in which we

estimate the probability that a user's information need is met

given a document as "evidence". The techniques required to support this kind of inference are similar to those used in expert systems that must reason with uncertain information.

Several probabilistically-oriented inference network models

have been developed using experimental document collections [5] during the past few years for information retrieval

[15]. These models are generally characterized by an

architecture with two layers corresponding to documents and

index terms. The documents and index terms are connected

by direct links. Initially, the prior probabilities of all root nodes (nodes with no predecessors) and the conditional

probabilities of all non-root nodes (given all possible

combinations of their direct predecessors) must be specified.

Aretrieval consists ofone or more documents with the highest

posterior probability for the given set of index terms (evidences) whidi represent a user's information need.

Over the last few years, the technique of automated

inference using probabilistic inference networks has become

popularwithin the AI probability and uncertainty community,

particular in the context of expert systems [6,7]. The most

<top> <head> Tlpstef Topic Description <num> Number 052 <dom> Domain: International Economics

<titie> Topic: South African Sanctions <desc> Description: Document discusses sanctbns against South Africa <nan> Narrative: A relevant document will discuss any aspect of South African sanctions, such

as: sanctions declared/proposed by a country against the South African government in response to its apartheid policy, or in response to pressure by an individual, organizatbn or another country; inten-ialional sanctrans against

Pretoria imposed by the United Naltons; the effects of sanctions against S. Africa; opposition to sanctions; or, compliance with sanctions by a company. The document will identify the sanctions instituted or being considered, e.g., corporate disinvestment, trade ban, academic boycott, arms embargo.

<con> Concept(s):

1. sanctions, international sanctfons, economic sanctions

2. corporate exodus, corporate disinvestment, stock divestiture, ban on new

investment, trade ban, import ban on South African diamonds, U.N. arms

embargo, curtailment of defense contracts, cutoff of nonmilitary goods, academic boycott, reduction of cultural ties

3. apartheid, white domination, racism

4. antiapartheid, black majority rule

5. Pretoria

<fac> Factor(s):

<nat> Nationality: South Africa

</fac>

<def> Definition(s):

<Aop>

Figure 2. Topic 052.

important constraint on the use of a probabilistic network is the fact that in general, the computation of the exact posterior

probabilities is NP-hard [1]. Thus it is unlikely that we could

develop an efficient general-purpose algorithm whidi would

work well for all kinds of inference networks. There are several alternatives, such as the use of approximation algorithms or heuristic algorithms, and creating special case algorithms [9,10].

The experiments here concern an attempt at a heuristic probabilistic inference network approach based on an AI

connectionist model. The connectionist model uses a competitive activation rule to find the most probable retrieval The term competitive activation rule refers to a spreading

activation method in which nodes actively compete for available activation in a network. An initial formulation of

a competitive activation mechanism was previously studied on three two-layer, abstract networks for diagnostic problem

solving [11,13]. The connectionist model proposed here

consists of a two-layer network ardiitecture. Document

nodes and index term nodes corresponding to each layer are connected by links whose weights represent association strengths between nodes. These links are also viewed as channels for sending information between nodes. Figure 4

is a simple network consisting of two document nodes and

three index term nodes. At each moment of time, each node

293
Topic 52

curtailment 001 201n.l 38n.2

individual 001 372a.O 372n.2 549a.0 79a.O 87a.O

87n.O

considered 001 611d.O

compHance 001 602n.3 743n.O 762n.O 772n.O

reduction 001 103n.l 144n.O 195n.O 201n.l

308n.l 813n.O 85n.3

economics 002 692n.3

corporate 003 43a.O

response 002 462n.O 586v.3 714n.O 821n.O

888n.2 990n3

relevant 001 23a.l 476a.2 9v.2

majority 001 102n.l 131n.O 33n.O

academic 002 514a.O 537a.O 542a.O

effects 001 780n.l0 780n.5 798n.O

defense 001 717n.O 937n.2

country 002 181n.O 189n.l 189n.8 189n.9

266V.1 344n.O 371a. 1

company 001 599n.l4 712n.2 726n.8 72n.2

88n.l 892n.4

policy 001 626n.2 692n.2

aspect 001 183n.O 448n.3 7n.O

white 001 429a.l 430a.O 441n.5 996n.5

stock 001 lln.3 153n.4 166n.2 225n.l3 25n.3

265a.O 31n.l 501n.2 613a.O 635n.O

636n.O 637V.0 637v.2 780n.l0

798n.O 800n.O 811v.O

goods 001 780n.l0 798n.O

black 001 349n.2 421a.O 431a.O 431n.3 432n.l

752n.O 945a.2

rule 001 136n.l 200n.l 466n.2 613n.5 693v.l

697n.l 737n.l 737v.2 737v.3 749v.2

80n.O 82n.3 963n.l

arms 002 459d.9 719v.2 722n.O 727n.O 877n.3

894V.3

international 003 12a.O 892a.4

organization 001 161n.O 329n.O 357n.O 357n.l 60n.O

opposition 001 179n.O 237n.O 708n.O 710n.O 719n.O

720n.O

investment 001 225n.O 716n.2 784n.O 809n.3

government 001 692n.3 693n.0 699n3 737n.l 737n5

domination 001 175n.O 737n.l

pressure 001 157n.2 175n.O 319n.O 642n3 735n.l

identify 001 13v.O 464v.l 480av.5

document 003 467n3 551n.2

embargo 001 265n.2 761n.O

discuss 001 298V.0 451v.O 460v3 476v.O

boycott 002 297v.2

another 001 104a. 1 15a.l 709v.O 714v.O

against 004 14a.O 179v.O 237d.O 276v.O 673d.O

704d.O 708d.O 708d.l 708v,0 708v.2

713V.2 716V.1 716V.5 717a.0 719v.0

764V.0 898a.O 932v.8

united 001 46a.l 714a.O

import 001 228v.l 296v.O 300v.O 516n.O 516v.O

642n.l 642V.0

exodus 001 293n.O 295v.l

trade 002 625n.4 734v.l 794n.l 794v.l

south 006 278n.l

being 001 In.O 3n.0 831n.0 976n.2

will 002 360V.1 600n.O 600v.0 602d.0 604a.0

604n.0 737v3 771n.ll 784n.4 new 001 123a.O 146v.O 18a.O 614a.0 66n.0

ban 004 761n.O 908n.O 98n3

any 001 25a.O 51n.O 609an.l

Figure 3: Word Frequency and Semantic Categories for Topic 052.

294
Figure 4: A Simple Network Consisting of Two Document

Nodes and Three Index Term Nodes.

receives information about the activation levels of its immediate neighboring nodes (nodes connected to it via

direct links), and then uses this information to calculate its own activation level Through this process of spreading

activation, the network settles down to equilibrium repre- senting a retrieval to a user's information need.

The computation of the information retrieval inference process is based on a formalization of the causal and probabilistic associative knowledge underlying diagnostic problem-solving [18]. We do not discuss the formulation

architecture and activation mechanism of the connectionist

model. This information can be found in [11,13,16,18]. For TREC-2, we managed to complete only one official routing

experiment for this approach, and it did not involve semantics.

The experiment was intended to be a baseline experiment for our semantic experiments.

For TREC-2, a specific network was constructed for 50

topics. A list of mdex terms was assembled based on keywords in the concept section ofeach topic. In this network,

each output node represented a topic, and each input node

represented a keyword. The prior probability assigned to each

topic node was equal to l/(total number of topics). The

connection strengths were assigned equal weights (0.9). The network contained 50 topic nodes and 848 index term

nodes. These nodes were connected via 1449 links. An

example of this network is shown in Figure 5, where is the prior probability of topic topi. The keywords "army",

"engineer", and "plant" were obtained by processing the concept section of topic top,. Currently, the network is enhanced by using an estimated weighting scheme.

We performed a Category B routing experiment. Using

just keywords, the results were not good. The main problem was due to the fact that, in the document ranking, many

documents had the same score used to generate the ranking.

In order to satisfy the requirements for the ranking, we had

to artificially rank those documents with the same score. This was done based on order of appearance. The performance was terrible except for Topic 66. This topic had only two

top i pl=0.02

(army ) (engineer ) (plant

)

Figure 5: A Sample Network of the Experimental Model

known relevant documents for (Category B routing experiments and our inference network retrieved one of them in the top 20 documents! No further connectionist model

experiments have been completed. Wewere unable to modify

the baseline keyword experiment or perform semantic

experiments for this approach.

4. Vector Processing JModel Experiments

In this section, we explain the manner in which semantics

is incorporated within a vector processing model using the semantic lexicon explained in Section 2. Please note that an

entry in our semantic lexicon has the form ofa word followed

by codes for each of the semantic categories the word triggers.

Weexplain our approach using a text relevance determination

procedure intended to show what is being calculated rather than show the actual computations for the approadi. The

procedure presented here generates several outputs that are reaUy not necessary, but are included just to help explain the approach. The relevance determination procedure is explained using the four documents and query shown in Figure 6. A few preliminary computations are reviewed in order to explain the procedure.

First, the number of documents eadi word is in must be

determined. Figure 7 shows a list of words from the four documents and the query of Figure 6 along with the number

of documents each word is in (df).

Next, the Inverse document frequency (idf) of each word

is determined by the equation \ogiQ(N/df), where iV » 4, the

total number of documents. Figure 8 provides the idfof each

word. Sometimes, the of a word is undefined. This can happen when a word does not occur in the documents but does occur in a query. For example, the words "depart", "do" and "when" do not appear in the four documents. Thus, the idfof these terms cannot be defined here. Later, we will see

that an adjustment can be made for these undefined values.

Next, the category probability of each query word is determined. Figure 9 shows an alphabetized list of all the unique words from the query, the frequency of eadi word in the query, and the semantic categories each word triggers.

295
word itff of the word

Document #1 '°SiOd/

t| ; . Locomotives pull the trains. and .6 canopy .6 carry .6

A. Document #2 depart undefined

[J ^ do undefined

1 ii People meet people under the canopy and within trains. freight .6 from .6

; hourly .6

iiJ . Document #3 leave .6

i'jj:

, locomotives .6

I ! Trains carry freight from the station. meet .6

\-x noon .6

i [ij people .6

;' I Document #4 pull .6 station .3 Trains leave the station hourly until noon. the 0

{ill trains 0

!'''!!, under .6 111 Query until .6

|;|, when undefined

III

' When do trains depart the station? within .6

Figure 6. Four Documents and a Query. Figure 8. The idfof Each Word.

word number of documents

the word is in (df)

and 1 canopy 1 carry 1 do 0

depart 0

freight 1 from 1 hourly 1 leave 1 locomotives 1 meet 1 noon 1 people 1 puU 1 station 2

the 4

trains 4

under 1 until 1 when 0

within 1

Figure 7. List of Words in the Documents and Query.

word frequency category probability

depart 1 AMDR 1/4

TAMT 1/8

do 1 AUSE 1/21

ATMP 1/21

TCSE 1/21

TCNV 2/21

TRES 1/21

TSRC 1/21

station 1 APOS 3/16

AORD 1/8

TAMT 1/16

TCND 1/8

TDGR 1/16

TSPL 3/16

the 1

trains 1 AORD 7/24

AMDR 1/12

AMFR 1/12

TACM 1/24

TCNV 1/12

when 1 TAMT 1/3

TTIM 2/3

Figure 9. Words in the Query.

296
The semantic categories in our example are those shown

in Figure 1. For example, consider the word "depart" which

occurs one time in the query as shown in Figure 9. The

semantic lexicon entry for the word "depart" using the categories of Figure 1 is as follows:

depart: NONE NONE NONE NONE NONE AMDR

AMDRTAMT

where NONE represents a word sense not included in the 36

semantic categories of Figure 1. If a uniform distribution is assumed, then AMDR is triggered 1/4 of the time and TAMT

is triggered 1/8 of the time. This is shown in Figure 9 as the

probabilities for each semantic category.

A similar category probability determination is done for each document. Figure 10 is an alphabetized list of all the unique words in Document #4 of Figure 6. The semantic

categories each word triggers along with probabilities are also shown.

The text relevance determination procedure is shown in Figure 11. The procedure uses three input lists:

a. List of words and the idfof each word, as shown in Figure

8.

b. List of words in the query and the semantic categories they

trigger along with the probability of triggering those

categories, as shown in Figure 9.

c. List of words in a document and the semantic categories

they trigger along with the probability of triggering those

categories, as shown in Figure 10.

The procedure operates as follows:

Step 1 .

This step determines the common meanings between the query and the document. Figure 12 corresponds to the output

of Step 1 for Document #4. In Step 1, a new list is aeated as follows:

For each word in the query, follow either subsection (a) or

(b), whichever applies:

a. For each category the word triggers, find each word in the document that triggers the category and output three things:

1) The word in the query and its frequency ofoccurrence.

2) The word in the document and its frequency of

occurrence.

3) The category.

b. If the word does not trigger a category, then look for the word in the document and if found, output two things and

a "—":

1) The word in the query and its frequency of occurrence.

2) The word in the document and its frequency of

occurrence.

word frequency category probability

hourly 1 TDM 1.0

leave 1 AMDR W

TAMT 1/7 noon 1 ALDM 1/3

'I'I'lX/f i i IM LIJ

the 1

station 1 APOS 3/16

AORD 1/8

TAVTT 1 /I ^ TCNP 1/8

TDGR 1/16

TSPL 3/16

trains 1 AORD

1 /I 9

AMFR 1/12

TACM 1/24

TCNV 1/12

until 1 TTIM 1.0

Figure 10. Words in Document #4.

Step 1 - Refer to Figure 12. Determine common meaning

between query and the document.

Step 2 - Refer to Figure 13. Adjust for words in the query that are not in any

of the documents.

Step 3 - Refer to Figure 14. Calculate the weight of a semantic component in the query

and calculate the weight of a semantic component in the document.

Step 4 - Refer to Figure 15. Multiply the weight in the query

by the weight in the document.

Steps - Refer to Figure 15. Sum all the individual products

of Step 4 into a single value whidi

is the semantic similarity coefficient.

Figure 11. Relevance Determination Procedure to Explain

Semantic Similarity.

297
First Ust

Item Number

12

3

4

56

78

9

10

1112

1314

1516

1718

1920

2122

23

24

First Entry Word & Frequency

in Query

(depart,!)

(depart,!)

(depart,!) (depart,!) (do,!)

(station,!)

(station,!)

(station,!)

(station,!)

(station,!)

(station,!) (station,!)

(station,!) (the,!)

(trains,!)

(trains,!)

(trains,!)

(trains,!)

(trains,!)

(trains,!) (wiien,!) (when,!)

(wlien,!) (when,!)

Second Entry Word & Frequency

in Document #4

(leave,!)

(trains,!) (leave,!)

(station,!)

(trains,!) (station,!)

(station,!)

(trains,!) (leave,!)

(station,!)

(station,!)

(station,!)

(station,!) (the,!)

(trains,!) (leave,!)

(trains,!)

(trains,!)

(trains,!)

(trains,!) (leave,!) (hourly,!) (noon,!)

(until,!)

Third Entry

Category

AMDR

AMDR

TAMT

TAMT

TCNV

APOS

AORD

AORD

TAMT

TAMT

TCND

TDGR

TSPL

AORD

AMDR

AMDR

AMFR

TACM

TCNV

TAMT

TTIM

TTIM

TTIM

Figure 12. Ctommon Meaning.

Second List

Item Number

12

34

56

7

8

9

10

1112

1314

1516

17

18

1920

2122

23

24

First Entry Word & Frequency

in Query

(leave,!)

(trains,!) (leave,!)

(station,!)

(trains,!) (station,!)

(station,!)

(station,!)

(station,!)

(station,!) (station,!)

(station,!)

(station,!) (the,!)

(trains,!) (trains,!)

(trains,!) (trains,!)

(trains,!)

(trains,!) (leave,!) (hourly,!)

(noon,!)

(until,!)

Second Entry Word & Frequency

in Document #4

(leave,!)

(trains,!) (leave,!)

(station,!)

(trains,!) (station,!)

(station,!)

(trains,!) (leave,!)

(station,!)

(station,!) (station,!)

(station,!) (the,!)

(trains,!) (leave,!)

(trains,!) (trains,!)

(trains,!)

(trains,!) (leave,!) (hourly,!) (noon,!)

(until,!)

Third Entry

Category

AMDR

AMDR

TAMT

TAMT

TCNV

APOS

AORD

AORD

TAMT

TAMT

TCND

TDGR

TSPL

AORD

AMDR

AMDR

AMFR

TACM

TCNV

TAMT

TnM

TnM

TnM

Figure 13. Adjustment for Words with no idf.

298
Considering Figure 12, the word "depart" occurs in the query one time and triggers the category AMDR. The word

"leave" occurs in Document #4 once and also triggers the category AMDR. Thus, item 1 in Figure 12 corresponds to subsection (a) as described above. An example using subsection (b) occurs in item 14 of Figure 12.

Step 2.

This step adjusts for words in the query that are not in any

of the documents. Figure 13 shows the output of Step 2 for Document #4. In this step, another list is aeated from the list created in Step 1. For each item in the Step 1 list which has a word with undefined idf, this step replaces the word in the

First Entry column by the word in the Second Entry column. For example, the word "depart" has an undefined idf2& shown

in Figure 8. Thus, the word "depart" in item 1 of Figure 12 should be replaced by the word "leave" from the Second Entry

column. This is shown in item 1 of Figure 13. Likewise, the words "do" and "when" also have an undefined idf and are respectively replaced by the words from the Second Entry

column.

Step 3.

This step calculates the weight of a semantic component

in the query and calculates the weight of a semantic component in the document. Figure 14 shows the output of Step 3

for Document #4. In Step 3, another list is created from the

list created in Step 2 as follows:

For each item in the Step 2 list, follow either subsection (a) or (b), whichever applies:

a. If the Third Entry specifies a category, then

1) Replace the First Entry by computing:

triggers the category

in the Third Entry ,

2) Replace the Second Entry by computing:

' frequency of 1 word in • word in •

iFirst Entiy^ ^ First Entry , \

' frequency of / word in * word in • ^Second Entiy^ ^Second Entry^

triggers the category

in the Third Entry ^

3) Omit the Third Entry,

b. If the Third Entry does not specify a category, then

1) Replace the First Entry by computing:

' frequency of word in • word in

^First Entry^ ^ First Entry ,

2) Replace the Second Entry by computing:

word in ^Second Entry^

3) Omit the Third Entry.

\ ( frequency of word in Second Entry^

In Figure 14, item 1 is an example of using subsection (a), and item 14 is an example of using subsection (b).

Step 4.

This step multiplies the weights in the query by the weights

in the document. The top portion of Figure 15 shows tlie output of Step 4. In the list created here, the numerical value

created in the First Entry column of Figure 14 is multiplied

by the numerical value created in the ^cond Entry column

of Figure 14.

Step 5.

This step sums the values in the Step 4 list to compute the semanticsimilarity coefficient for a particular document. The

bottom portion of Figure 15 shows the output of step 5 for Document #4.

We have finally observed an improved Precision/Recall

f>erformance using the semantic similarity coefficient explained here. For example, in a Category B filtering

experiment where the words being considered were only those

in the topics and idf values were determined by the number

of topics a word is in, we have observed the keyword and

semantic results shown in Figure 16 and Figure 17, respectively. The U-pt average for these two experiments reveals a 23% increase due to the use of semantic categories.

According to Sparck Jones' criteria, this change would be

classified as "significant" (greater than 10.0%) [12]. We

believe further improvement is possible by considering more

words, stemming for plurals and tenses of words, better idf values (hke those used for archival retrieval), a modem

lexicon, and a focus on paragraphs instead of whole documents.

5. Summary

Our progress during TREC-1 and TREC-2 has been the following:

a. We created efficient code for a UNIX platform. Originally our code used B+ tree structures for implementing inverted

files on a DOS platform. We now use hashing to replace

B+trees, establishing codes to replace character strings;

and the UNIX platform provides faster processing than the DOS platform.

b. Webuilt an index for a semantic lexicon based on the public

domain 1911 version of Roget's Thesaurus. To do this, we had to aeate our own category numbering system

similar to today's version of Roget's Thesaurus.

c. We solved part of the blend problem for semantic and

keyword weights. Wenow base semantic category weights on the i<i/of words which generate the semantic categories.

We can now index or scan TREC documents at rates faster than 60 Megabytes per hour depending on the workstation.

We have a semantic lexicon of approximately 20,000 words

with flexible category codes that allow a course (36 categories) through fine (more than 15,000 categories) semantic

analysis. As shown in Section 4, our procedure for determining relevance is based on the senses of each word. For example, using the vector processing model and the

similarity coefficient

y.i

299
Third list

1 Number First Entry Second Entry

1 .6* 1*1/7 = .0857 .6* 1*1/7 -.0857

2 0*1*1/12 = 0 0*1*1/12-0

3 .6*1*1/7- .0857 .6 * 1 * 1/7 - .0857 4 .3 * 1 * 1/16 - .0188 .3 * 1 * 1/16 - .0188 5 0*1*1/12-0 0*1*1/12-0

6 .3 * 1 * 3/16 - .0563 .3 * 1 * 3/16 - .0563 7 .3 * 1 * 7/24 - .0875 .3 * 1 * 7/24 - .0875 8 .3 * 1 * 1/8 - .0375 0*1*7/24-0

9 .3 * 1 * 1/16 - .0188 .6* 1*1/7 -.0857

10 .3* 1*1/16 -.0188 .3* 1*1/16 -.0188

11 .3* 1*1/8 -.0375 .3 * 1 * 1/8 - .0375 12 .3 * 1 * 1/16 - .0188 .3* 1*1/16 -.0188

13 .3 * 1 * 3/16 - .0563 .3 * 1 * 3/16 - .0563 14 0*1=0 0*1=0

15 0*1*7/24-0 0*1*7/24-0

16 0*1*1/12-0 .6* 1*1/7 -.0857

17 0*1* 1/12-0 0*1*1/12-0

18 0*1*1/12 = 0 0 * 1 * 1/12 - 0

19 0*1*1/24 = 0 0* 1 * 1/24 = 0

20 0*1*1/12-0 0*1*1/12 = 0

21 .6 * 1 * 1/7 - .0857 .6* 1*1/7 -.0857

22 .6 * 1 * 1.0 - .6000 .6* 1*1.0 = .6000 23 .6 * 1 * 26 - .4000 .6*1 *2/3 = .4000 24 .6* 1*1.0-.6000 .6*1* 1.0 -.6000

Figure 14. Weights of Semantic Components.

Fourth List

Item Number Value

1 .00734

2 0

3 .00734

4 .00035

5 0

6 .00317

7 .00734

8 0

9 .00170

10 .00035

11 .00141 12 .00035

13 .00317

14 0

15 0

16 0

17 0

18 0

19 0

20 0

21 .00734

22 36000

23 .16000

24 .36000

Sum of all values in Fourth List 0.91986

Figure 15. Multiplied Weights and Their Sum.

300
Queryid (Num): 47 of 50

Total number of documents over all queries

Retrieved: 36610

Relevant: 2064

Rel_ret: 913

Interpolated Recall - Precision Averages

at 0.00

at 0.10

at 0.20

at 0.30

at 0.40

at 0.50

at 0.60

at 0.70

at 0.80

at 0.90

at 1.00

0.3514

0.1968

0.1367

0.1082

0.0894

0.0752

0.0276

0.0105

0.0062

0.0013

0.0007

Queryid (Num): 47 of 50

Total number of documents over all queries

Retrieved: 36383

Relevant: 2064

Relret: 956

Interpolated Recall - Precision Averages

at 0.00

at 0.10

at 0.20

at 0.30

at 0.40

at 0.50

at 0.60

at 0.70

at 0.80

at 0.90

at 1.00

0.3961

0.2479

0.1734

0.1258

0.1067

0.0838

0.0372

0.0195

0.0100

0.0029

0.0009

Average precision (non-interpolated) over all rel docs

0.0746

Precision:

Average precision (non-interpolated) over aU rel docs

0.0919

Precision: At 5 docs: 0.1660 At 5 docs 0.2426

At 10 docs: 0.1532 At 10 docs 0.2149

At 15 docs: 0.1433 At 15 docs 0.1801

At 20 docs: 0.1298 At 20 docs 0.1574

At 30 docs: 0.1057 At 30 docs 0.1383

At 100 docs: 0.0643 At 100 docs 0.0745

At 200 docs: 0.0465 At 200 docs 0.0522

At 500 docs: 0.0302 At 500 docs 0.0320

At 1000 docs: 0.0194 At 1000 docs 0.0203

R-Precision (precision after R (= num_rel for a query)

docs retrieved): Exact: 0.1035

R-Precision (precision after R (= num_rel for a query)

docs retrieved): Exact: 0.1283

Figure 16. Filtering Using Keywords. Figure 17. Filtering Using Semantic Categories.

if the word "trains" is in the Query and the word "leaves" is in the Document and we look at the semantic category Motion

with Reference to Direction (AMDR), then one of the vector product elements in the formula becomes:

(weight of *tnint*| / prabability \ , /weight of *leaves'\ / ptobabilty \

in Qaeiy ) i'tniu' trigger* AMDR j I in Document } \ 'leaves' triggers AMDR )

where the probabilities are obtained from our semantic lexi- con.We plan to do more experiments incorporating the fol- lowing improvements:

a. Modernize the semantic lexicon. Since our lexicon is based on the 1911 version of Roget's Thesaurus, many modem

words are not present and the senses of recorded words are not accurate. We plan to correct this. For example, we

could try to get permission to use the current version of

Roget's Thesaurus.

b. Base similarity on paragraphs instead ofwhole documents.

We have bad success using as few as 36 categories in a paragraph environment. We also feel that relevance

decisions are made by humans looking at roughly a paragraph of information. We plan to modify our code to use paragraphs as a basis for the similarity measure.

c. Experiment with the number of possible semantic cate- gories and the probability assigned to a triggered category.

The experiment behind the performance improvement

shown in Figure 16 and Figure 17 uses a very fine number

of semantic categories and treats the triggered semantic

categories for a word uniformly. We plan to experiment

with a fewer number of categories, and we plan to obtain a probability distribution for categories based on word

usage.

Basically, we are trying to establish a statistically sound

approach to using word sense information. Intuition is that word sense information should improve retrieval performance. Furthermore, our approach to using word sense infor- mation has shown a significant performance improvement in a question/answer environment where paragraphs represent

documents. We feel that other word sense approadies, such

as query expansion or word sense disambiguation, may not be statistically sound, and that may be why successful experiments have not been reported.

301
References

[1] G. F. Cooper (1988). Probabilistic inference using belief networks is np-hard. Technical Report KSL-87-27,

Stanford University, Stanford, CA.

[2] W. B. Croft (1987). Approaches to intelligent infor- mation retrieval. Information Processing & Management, 23(4):95-n0.

[3] C. Date (1990). An Introduction to Database Systems,

Vol. I, Addison Wesley.

[4] J.Driscoll,L.LautenschlagerandM.21iao (1993). The

QA system, Proc. ofthe First TextRetrieval Conference

(TREC-I), NIST Special Publication 500-207 (D. K. Harman, editor).

[5] E. A. Fox (1983) Characterization of two new exper- imental collections in computer and information science

containing textual and bibliographic concepts.

Technical Report 83-561, Department of Computer

Science, Cornell Univ., Ithaca, NY.

[6] L.N. KanalandJ. F. LemmerEds (1986). Uncertainty

in Artificial Intelligence, North-Holland, Amsterdam

.

[7] J. F. Lemmer and L. N. Kanal Eds (1988). Uncertainty

in Artificial Intelligence 2, North-HoUand, Amsterdam.

[8] E. L. Liddy and S. H. Myaeng (1993). DR-Link, Proc. of the First Text Retrieval Conference (TREC-I), NIST

Special Publication 500-207 (D. K. Harman, editor).

[9] R. E. Neapolitan (1990). Probabilistic Reasoning in Expert System, Theory and Algorithms, A WileyInterscience Publication, John Wiley & Sons, Inc.

[10] J. Pearl (1988). Probabilistic Reasoning in Intelligent

Systems: Networks ofPlausible Inference. San Mateo,

CA: Morgan Kaufmann.

[11] Y.PengandJ.A.Reggia (1989). Aconnectionist model

for diagnostic problem solving. IEEE Transactions on

Systems, Man, and Cybernetics, 19(2):285-298.

[12] K. Sparck Jones and R. Bates (1977). Research on

Automatic Indexing 1974-1976, Technical Report,

Computer Laboratory, University of Cambridge.

[13] M. Tagamets, J. Wald, M. Farach and J. A. Reggia

(1989). Generating plausible diagnostic hypothesis with

self-processing causal networks. Journal of Experimental Theories in Artificial Intelligence, 2:91-112.

[14] Roget's International Thesaurus (1977). Harper &

Row, New York, Fourth Edition.

[15] H. Turtle and W. B. Croft (1991). Evaluation of an

inference network-based retrieval model. ACM Transaction on Information Systems, 9(3): 187-222.

[16] P.VanLaarhandE.Arts (1987). SimulatedAnnealing:

Theory andApplications. Boston: D. ReideL

[17] D. Voss and J. DriscoU (1992). Text Retrieval Using a Comprehensive Semantic Lexicon, Proceedings of

ISMM First International Conference on Information

and Knowledge Management, Baltimore, Maryland.

[18] P. Wang, J. Reggia, D. Nao and Y. Peng (1985). A

formal model for diagnostic inference. Information

Sciences, 37:227-285.

[19] E. Wendlandt and J. DriscoU (1991). Incorporating a Semantic Analysis into a Document Retrieval Strategy,

Proceedings of the Fourteenth Annual International

ACM/SIGIR Conference on Research andDevelopment

in Information Retrieval, Chicago, Illinois, 270-279.

302
The Efficiency Issues Workshop Report

James P. Callan

Computer Science Department

University of Massachusetts

Box 34610

Amherst, MA 01003-4610, USA

(callan@cs.umass.edu)

Although most groups participating in TREC-2 empheisized precision and recall, the conference was also an appropriate forum in which to discuss the efficiency

of document indexing and retrieval. For some participants, running out of disk space was their worst problem, while for others running out of time Wcis. Some

groups were unable to run on the entire collection, and

several remarked that they were happy to have gotten

anything running at all. No group reported finding

TREC trivial. Two discussion groups were organized to address ef- ficiency issues, one focusing on document indexing, the

other on document retrieval. Since efficiency has not been emphasized by the research IE community, participants in both groups felt that current cdgorithms

have a lot of room for improvement. TREC provides one motivation for such improvements, as do ever- growing real world databases. However, there was con- cern in both groups that the TREC format, which en- courages participation in both ad hoc (retrospective)

and routing (filtering) tasks, might discourage research on efficient task-specific architectures.

The following sections provide more detail on each

of the two discussion groups.

1 Document Indexing

The raw text for the TREC collection (routing and

ad hoc) required approximately 3 GB of space. Index

structures required from 7.550themselves sufficient to recreate the original text, so they would be additional

overhead in an operational system. (A research system

might be able to discard the original text, reporting

just document ids for evaluation.)

Several groups, including CITRI, Thinking Machines, and UMass, stored inverted lists in compressed

form. There was general agreement that for sites will- ing to invest the programming eflTort, substantial space

savings could be achieved in this fashion. (CITRI

demonstrated a factor of six reduction in index file size.) There was more debate on the potential for in- dex compression speeding up query processing as well,

with some peirticipants saying their query processing was I/O bound, but others saying theirs was CPU

bound. The peak amount of space used during indexDavid D. Lewis

AT&T BeU Laboratories

600 Mountain Avenue

Room 2C409

Murray HiU, NJ 07974, USA

(lewis@reseeurch.att.com)

ing (for text, indices, and auxilieiry files) varied from

llOthat this may be worth more attention.

Efficiency improvements will not come immediately,

and some may require significant expense in programming time. Shciring of softwcire between groups, which

increased from TREC-1 to TREC-2, helps limit this expense. In addition, TREC research groups primarily interested in, say, query analysis, may in the future

want to team up with groups that have addressed or

are addressing issues of scale. As the size of test col- lections increases, it makes less sense to have them

replicated at dozens of sites, particular when interactive access across networks is usually easily available. Time to build index structures was tolerable for most participants, though it was mentioned that it never seems to go as easily or automatically as one might hope. It was a serious issue for groups doing some form of natural language processing. Times of

2 to 4 MB/hr were mentioned by at least two of the

NL groups, making TREC a very daunting task. The

opinion was expressed by some participants that an NL technique would have to provide as yet undemonstrated improvements in effectiveness to be worth the

slowdown in indexing and query processing.

Complex text representations, such as those pro- duced by NL, require additional information in the

index structures. The different ways of dealing with

this problem axe most noticeable in the handling of

phrases in the TREC, with some groups indexing on

phrases just as on words, others relying on word position information stored in an inverted fUe, and still others reparsing the raw text of a subset of documents

at retrieval time to find phrase occurrences.

2 Document Retrieval

The second discussion group was organized around

general issues in document retrieval. Participants were encouraged to use their experience with the one and

two gigabyte TREC collections to forecast the issues that will arise when collections are larger and more

distributed. Two issues dominated the discussion.

303
2.1 Will Existing Methods Scale?

Recent trends in information retrieval are towards gigabyte and terabyte document collections, retrieval by

subsections/paragraphs, and multiple representations

of document content. The first focus questions were whether existing storage and retrieval methods can

cope with this explosion of information, and if new

methods are needed, what might they be?

Participants identified two approaches as currently

dominating IR:

1. Search all available subcollections (e.g. the TIPSTER/TREC task), or

2. have a user specify which subcoUection to secirch

(e.g. commercial systems).

Both approaches require modification if they axe to

scale up. One problem with the "search everything" approach

is that the growth rate of online information was felt to exceed the growth rate in computer performance.

Even if a collection is distributed across multiple processors, detailed consideration of every document may

be too expensive when an IR system faces a terabyte of

data. One solution is to do a fast first-pass retrieval to produce a reduced set of documents for more detailed

consideration. This first pass might involve generating approximate scores for each document (e.g. ETH

in TREC-2), scoring documents based on a smaller

amount of text (e.g. abstracts or introductions), or scoring cluster centroids rather than individual documents.

One problem with the "user chooses subcoUections"

approach is that the task wiU be overwhelming when

there are many subcollections. A significant portion

of users of one commercial service already choose to search everything rather than select subcollections.

The system wiU have to provide assistance if user se- lection is to be viable. If subcollections can be characterized succinctly, perhaps by centroid vectors, auto- matically generated thesauri, or controlled vocabulary

terms (assigned manually or automatically), then one

could use the query to rank subcollections, and then

search only the top-ranked subcollections. Other ap- proaches include assistance by an expert system, or browsing interfaces for hyperlinked subcollections.

Global statistics that summarize some aspect of a

collection (e.g. idj) were expected to be a problem for searching multiple subcollections and distributed document collections. If a collection is formed at indexing

time, statistics can be gathered and saved when in- dices are bmlt. If a single processor performs retrieval,

statistics can be gathered during retrieval. However,

if multiple subcollections or processors cire involved, it is less clear how to compute global statistics. Methods that rely only on local statistics that summarize a document (e.g. Berkeley in TREC-2) offer a computational advantage in this environment.

2.2 Specialized Hardware and Software

A related question posed by increasingly laxge and dis- tributed text databases is whether existing hardware

and software platforms wiU be up to the challenge. The

consensus among participants was that conventional

architectures wiU suffice, because IR is a data-parallel

task that lends itself to distributed computation. Participants also felt that they had generally ignored is- sues of efficiency, and could increase their speeds if necessary. There was little support for supercomputers, massively parallel computers, or specialized architectures. One could argue that the participants were biased

towards conventional hardware and software by their own need for flexibility, their small budgets, their insulation from the time constraints of real users, and their desire not to think about 'systems' issues not directly relevant to their research. However, the recent fielding

of ranked retrieval systems using conventional mainframes by some of the largest online vendors provides

additional support for their views that conventional

architectures wiU suffice.

304
Workshop Report

Use of training materials in constructing routing queries

William S. Cooper

Stephen £. Robertson

The participants outlined various methods of exploiting the training data for routing retrieval that had been used in the conference. In all cases the data had been used in a topic- specific manner; i.e. each query was constructed or expanded using relevance judgements for that particular topic only.

In some systems, terms taken direcdy from the topic were weighted or reweighted using the training data. In others, terms taken from the training documents relevant to the topic were used in addition to topic terms, or were used instead

with the original topic terms playing no part. In a few cases, terms from both relevant and non-relevant documents were

added, the latter with negative weights. Relevant documents with a high preliminary retrieval ranking coefficient were

preferred as a source of expansion terms ua one system. Probabilistic, feedback and ad-hoc methods had all been tried as ways of modifying the query in response to the training data. How far might a query profitably be expanded on the basis of the training data? Though this question was not answered definitively, some participants indicated a greater willingness to consider drastic expansion than had been

thought advisable before TREC 2.

The sample of relevance judgements for TREC 2 was thought to be adequate in size and not imreaUstically large. It was sufficiendy representative in its inclusiveness of feedback generated from a wide variety of systems. However, tiiis variety indicates a possible lack of realism, m tiiat a real system would probably have access only to relevant documents

retrieved by itself. Thus the use of only those relevant documents found in a search on the training data by the system in question might be regarded as more realistic.

305

APPENDIX A

This appendix contains tables of results for all the TREC-2 participants. The tables in Appendix A show

various measwes of the performance on the adhoc and routing tasks. The adhoc results come first, followed by

the routing results, with the tables in the same order as the presentation order of the papers. The definitions of

the evaluation measures are given in the Overview, section 4. and readers unfamiliar with these measures should

read that section first.

Care should be taken in comparing the tables across systems. These measiues show performance only, with no measure of user or system effort.

Each table contains four major boxes of statistics and three graphs.

Box 1 " Summary Statistics

line 1 " unique run identifier, data subset, and query construction method used

Data subset

full (disks 1 and 2 for adhoc, disk 3 for routing)

category B (the official subset of data, 1/4 of the data using the Wall Street Joumal articles for adhoc

and the San Jose Mercury News articles for routing)

Query construction method

automatic

manual

feedback (frozen evaluation used)

line 2 ~ Number of topics included in averages.

line 3 ~ Total number of documents retrieved over all topics. Here, "retrieved" means having a rank less 1001.

line 4 ~ Total nimiber of relevant documents for all topics in the collection (whether retrieved or not).

line 5 -- Total niunber of relevant retrieved documents for this run.

Box 2 — Recall Level Averages

lines 1-11 ~ The average over all topics of the precision at each of the 11 recall points given. Note

that this is interpolated precision: e.g.. for a particular topic, if the precision at 0.50

recall is greater than the precision at 0.40 recall, then the precision at 0.50 recall

is used for both the 0.50 and 0.40 recall levels.

line 12 — The average precision as calculated in a non-interpolated maimer

(see section 4 of the Overview for details on this calculation).

Box 3 ~ Document Level Averages

lines 1-9 ~ The average recall and precision after the given number of documents have been retrieved.

line 10 - the R precision. This is a new evaluation measure being tried that averages the precisions found for each topic at the document level of R,

where R is the number of relevant documents for that topic.

A-1
Box 4 - Fallout Level Averages

lines 1-11 " The average recall (probability of detection) at a fixed fallout (probability of false alarm rate). Ten equally spaced fallout points in the high precision end of the curve were used, and for each topic, the highest recall value in the region surrounding that fallout point is selected. The table shows the averages of these points across all topics.

Graph 1 -- Recall-Precision C!urve

This is a plot of die data shown in Box 2.

(jraph 2 -- Fallout-Recall CXirve

This is a plot of the data shown in Box 4.

Graph 3 ~ Normal Deviate - Fallout-Recall

This is a plot of the data shown in Box 4. but plotted on probability scales.

A-2
3

c

'5

0::

0)

3

o

niu

to

>.

k_

niE

E

3

o o *J IT)

raE

o-<-> 3

n

v>

-fa

c E

3 3

o ir> O OO t-H oO O <£>

Lf) I—

I

« C •

•

(U TO O) V « O) a: oc: od

OO^-OOCNOvOCM

ooooodood

in in u)

in in in in O LD

Lft I—I <—

I

o o CN CO o o o o CM to <<<<<<<<<

<i> n>

£^ .!2

M-

(0c

o

OT > 0)

= >-

5i ^ -a G 4? E

4)

' O c

00^000000"—iLft<T)r^i—lo ^CNOJOOCSIOiCOO^COCNO

h-'*oorM<N<N--it-Hooo ddddddddddd

ooooooooooo o<-iCM(r)'*LOvor«--coo^o dddddddddd.--i

n

k.<u>

o

c

o

'in (J in

0)

k_ u

a. O

4J <-> u> c

nj ra>

>< 0)

0)hO

ra CO T—l t—

1

oo 00 00 00 00

ra CO r~- 00 CO CO CO CO CO

0) u CO t—

<

CO > 0} CO ir>

Level

Od o o o o o o o o o o o

4-> M o o o o o o o o o o o

3 3 o CN '4- oo o CM vO oo o

_o o o o o o 1—

H

<—

(

I—

1

1-H <N

_0 o o o o o o o o o o o

Fal "ra o o o o o o o o o o o

o o o o o o o o o o o

A-3
>

o

V

od^dd o dddd is —

Q.

>.

njE

E

^ o

n

.4>

1—I LO

CN CO O CN

<-! O O

<U 0) O) Qi en

oor^oo'^csioiLTi CN'*CNLf)00O'£)^-N- a>O00h-vOO<-lcy)CN

W 10 </) U) V) l/> U) O O CM CO >—I CM LO i-H <<<<<<<<<

01 .12

n

c

o

c

O *> CO ^ -i S

>

<u 2 --^ E

c

-S ^ E

OO'^OCMO^LO''^'—ICM"*0 CM'sd-OOLftOOOOOOOOOO oocn^LTioocMtr^cri'^.-HO r^'^'>:J-OOCMCM^OOOO

ooooooooooo o.-HOJco'^LOvor^oooio

TO m

<uW) 00 CM CM CM CM CM CM CM

nj

TO I—

I

oo oo I—

1

T—

1

I—

1

I—

1

T—

1

<u <J CM 00 PO o o o o o > <U <n LO LO LO <£>

evel A q; o o o o o o o o o o o

1 !-> -M o o o o o o o o o o o o CM 00 o CM vO 00 o o o o o o »—

1

I—

(

I—

1

I—

1

CnI o o o o o o o o o o o "75 o o o o o o o o o o o

u_ U- o o o o o o o o o o o

A-4
to

niE

E

to

ie> CM

ai 01 E

C E

O CTi OO o i-t r~ O »-( PO

L« ^ CN CN

« c •

• S 5 «

*-> — —

I

4) 4) 4) OH 0^ Ql

o o o ON O ^ vo vo O O <£) CN 0>

CM 00 CTl CO OO

If) CN CO

CO CN I ooooooooo

in (n u) in o ir>

o o o o CN CO <<<<<<<<<

OH .2

Od

Z. C

!2 g

~ 4) « E

Q- in

I 'J

if)r^'*LOT-ir»-CNr»-<—(lO'rt- ^.-HCT)CN'*OOLftO'*0 «-ivooocooooioor»-co»-i ^ CO CO CN CN i-H >-H O <p O C30000000000

o o o o O rH CN CO o o o o o o o ^ if5 «o r*- 00 o> o OOOOOOOOOOi-i (U ^-> bO C

ni TO ^2

COCOt-H'*.—lOCNCNCNCNOJ i-ILDCOOOOCJiOOOOO

Td-'<a-.-ILftCJ)OCNCNCNCNCN CO^LrtlOLDkO'^OvOOiCi

cbcDCDOocscijocboo

ooooooooooo ocN'a-ioooocN'>r^ooo OOCSOOi-Hi— ooooooooooo i<-H>—It-hcn oocicboocboocDcb

A-5
>

c

oo^oo o odoo

Q.

>>E

E

3

C/)

CM

c E

r-H CT> O <—( >—

I

<-H CO

CN

<u c •

-M -2! -J

0) 01 0) Qc oc ai

OOh-OP0CN<X5CX3O Ojooooopooiir).-HCN oioot^oor^corM>!d-ai

ooooooooo

w in w w) (/)</) V)

O O CN CO o oo CM in <<<<<<<<<

a:

TCLD'i^vOCMO^I^PO^O'^O

LD-—ir-cMLOLftvomoioo OCMLTl^-.-l'^O^-'^-CMO •O'^COCMCM.-I.—lOOOO ooooooooooo

ooooooooooo 0'-HCMcO'a-Lr)i£)r^ooCT>o oooooooooo^

<U (J

bO c

V)

0)bO 00 1—1 o in CO CO CO CO CO CO asw nj <n in CM VD CO CO CO CO CO

a> <j CM CO OO CO \o OO O) > v CO in in in in m in in in

evel A Od o o o o o o o o o o o

1 *-> o o o o o o o o o o o

3 3 o CM vO OO o CM 00 o _o o o o O o I—

1

i-H I—

1

r—

1

I-H CM _o o o o o o o o o o o o

"to H o o o o o o o o o o o

LL. u_ o o o o o o o o o o o

A'6
O (/> o ID

LO V 00 CN m

' OJ E

V o oo 3 IT) .—1 o tr

<-> 3

OJ "(5

ics "5 > M o 1 vt 'M

ni IL2-

:nts c

«/) o E

3 k. (J ra o

E -o E I/) O 3 0)

ber

ueri

iber

IMumI a<*-o

E

3

C eved: ^ant: a> c E

"(5

•i-> tri J) 1 3 3 0) OJ a: a:

ooeoofOOCNOLO

Lf)Lr5LOLO'!d-'>NrrO<N>—

(

ooooooooo

u) in ui

o o CM oo o o

CM in <<<<<<<<<

cm .S2

on o

0)

U1 £

on

lOr-HCMT—(CyjLDi—lh-LO'*C» LDOOCOCNLOOOO^LDOO"*

t>-CDh-0OCDLf)C7>C>Or>-C?>i-< h-LO^'^OOfOCMCN'—100 ooocioocioooo

00000000000 0<-ICNCD'>rLf)vC)I>-000>0 oocaooooooo^--8

bO c ^2

V)

<uhO (TO r~- 00

(TJ ro cn 00 00 CO 00 <o

(U u CTi CM <£> 00 00 00 00 00 00 > a> iD >~ r-- P>-

evel A on 0 0 0 0 0 0 0 0 0 0 0

1 +-> -M 0 0 0 0 0 0 0 0 0 0 0

3 3 0 00 0 CN 00 0

_o 0 0 0 0 0 i-H I—

1

I—

(

i-H 1—1 CN _o 0 0 0 0 0 0 0 0 0 0 0 (TJ 0 0 0 0 0 0 0 0 0 0 0 u. 0 0 0 0 0 0 0 0 0 0 0

A-7
4>>

en

00£-00 .2 oooo '.— —

nM

to

>»

niE

E

3

to

o o

E

oM

3

CO

S 0) E

c E

3 3

a: z

o in 00 O 00 "-H o o O O 00

ID >-(

>

= M — ! <t) 01 OJ a: ct: id::

OOh-OCO'^CriCT)'* OOCNOOCO'-H(r)OOfNO

LDUnLDLftLO^OOCN-—

I

ooooooooo

(/> U) U) l/> (0 Vl I/) o O O

O -a -a

"'^ O Lf)

Lf) I—I >-H o o CM on o o oCN IT) <<<<<<<<<

Qd .12 5

!2 g

0)

o « E ^

JJ 4, <U q; -g :S E

Q.

'^CTiCTiOOi—i"^Oi-HvOi—IVO '*oeooo^--';l-Lf><*Lr)vDt^

r^LTi'^-'sj-fOfiorsicN^oo ooooooooooo

ooooooooooo Or-icNpO'^-Lovor^ooaio 0000000000.-I

4) (J

<U !-> bO c

Averages Recall

evel -J

out lout

Fal Fal

^OLOlOOO'^LriOOOOOOCDCO

T—li—tO'^fr-H^^-r—li-Hi-Hi—It—

1

Lrioo<£><—iLf)^t^r«.t^r^r^

ooooooooooo

ooooooooooo OCN'^-vOOOOCN'sd-vOOOO 00000<— OOOOOOOOOOO It—l>—Ir-it-iCN

ooooooooooo

A-8
3

u

V

u

a:

to

>,

niE

E

3

O

I

00

c E 3 3

O Lft vD

OO o r— o LO I—

I

« C •

• a> TM 0)

> — ^ h— i: _|

0) a> o) ci: 01

ooro-oroooLTiLfipo OvOOOOOO^OOiOOLO

ooooooooo

I/) U) V)

I/) 10 (/) W <J <J (J O "O -U

''^ O Lfi

Lf) >—I <—

I

o o

oj CO o o oCM LD <<<<<<<<<

r^ovDeovoN-coiooooo

'a-ioo'^cr)^<:r)>-H">i-LDo OOLDLD'd-pOPOCNCN^OO ooooooooooo

ooooooooooo Oi—ICNCO'*lf)i£>t>-OOaiO OOOOOOOOOOt-H

0) ^

0)hO CN LO I—

1

LO LO LO LO

TO

ro CM 00 o CN I—

(

fO OO OO PO OO o >—

1

•~o LD LO > 0) ^ \r> r»-

evel A O o o o o o o o o o o

1 •M M o o o o o o o o o o o

3 3 o CM 00 o CN <c OO o _o o o o o o I—

1

I—

1

r-H r-H _o o o o o o o o o o o o

"ci "(i o o o o o o o o o o o

U- U- o o o o o o o o o o o

A-9
>

co

OOf-OO o oooo '.— —

niEE

3

to

o o

E

o

55 <u o 3

c E

o in00

r>- o

<0 C • > ra *^

.SJ > M — ! 4) 4> OJ q:

OOOOOOCNT-tOlLT)

c-joo-—ir-icsj<£>oocoir)

>—ICNOJCMi—(inr^T^Lf) LDLDLOLDLO'vfCOCN'-H ooooooooo

(/)(/) u) in

(/) U> lO

o o CN CO o o

CM Lf) <<<<<<<

c 2 c

O <U 03 S^ -"^ >

.12 5 w

4) 2: S b

> ,

c

q: ^ £

>^-oa)OOTi-.-i(r>oh-oo CM<-H00(^LnCNCNC0'*O<O or)'*a>fooooocx3^oor«-o r^Lf)"^!-'*C0C0CN04<-iOO ooooooooooo

ooooooooooo

OOOOOOOOOOr-H

>

O

c

O

'u)

'<J

0) CS

V.Q. o

-o

0) +-I bO c

ra nj

i_ >

>< OJ

i-i o .-( M CO

<ubO O r—

1

o CO Ol 1^

OJ

TO oo o CM r—

I

I—

I

LT) in If) Lf) Lf)

(J CT> oo Lf) in LO Lf) Lf) If) > <U IT) r>- r«.

svel A ct:: o o o o o o o o o o o

-J o o o o o o o o o o o

3 3 o CN 00 o CM oo o

_o o o o o o 1—

(

I—

1

I—

1

CN

_o o o o o o o o o o o o

Fal "ra o o o o o o o o o o o

UL o o o o o o o o o o o

A-10
o o LO o>

'M o 00 LTl

* ^ o CN E o o 00 3 LD 1—1 o o- •*-> 3 "(5

ICS "5 >

o

1/1

tati a

+Jc

to o E

3

w. O

nj o E -o E VI <+-o

CO

-Q

'ueri iber £ a t -b

Nu o

3

C > ran 4-1 <u c E

~n

•M -*-» — 1 3 3 V a> h2 01 on

oot^ocovDcj>r~-CN

'£>'«3-irM'«d-'-iCNir>OLf> LOtDLOLOLD'a-fOfM"—

I

ooooociooo

V> W) I/)

O O CM CO o o oCM LO <<<<<<<<<

q: .!2 J,

"Jc 2 c

(J —

' O

<u

E

c ^ E

COOOOCN'^lDOOOOLD^-vO r»-<-H<£>r^^pO'd-co^oh-oo <£)Lf)OLft'-HiX>0<riLDh-.0

ooooooooooo

ooooooooooo Or-if\i(r)«a-Lf>v£>r~-00CT)O OOOOOOOOOCZ)r-i

V o

bO C

ni nj

C*^ 1-1 o

1/) 0)bO Lf) 1 <^ CO o OO OO OO OO OO m

nj CN LT) CO Lf) o o o o o 0) <j cy> r«- CN 00 > 0) '3- LO r>-

evel A o o o o o o o o o o o

1 4-» 4-* O o o o o o o o o o o

3 3 O '3- vO 00 o CN OO o

_o O o o o o I—

(

1—

t

I—

(

i-H CM _o

"(5 O o o o o o o o o o o o o o o o o o o o o o

Ll_ U- o o o o o o o o o o o

A-11
mo

or:

3

n

u

to

>>£

E

u o

E

o

1—1 Oo

a

c E

3 3

O lO <-H OO 00

1^ CN O 00 m r-H

5 5

OJ 4) 01 ct: Q:: ct:

OOOOfO'^CTiLDO

'-HCT>oooor^o>o<oio

ooooooooo

I/) (/>(/) (/) o o CN CO o o o o CN Lft <<<<<<<<<

c j; c

O <U -= > W > 4) V !r

0 .i 4i 5) i: -Q

"o « E

1 o ^ § a: -a "5 E

CN<T>'^oocor^'-Hcovoooo ooLOLO'Nr^cofocMr-ioo ooooooooooo

ooooooooooo O'-HCNcn^a-inior^ooo^o OOOOOOOOOOr-i

O) (J

bO c

ni ni

O .-I CM CO

(/) <ubO CO r— CO o CO CO CO CO CO

(Q

k_ (D <£) o .—

1

I—

1

CO CO CO CO CO

(J r-l O) r>~ OO CD > <U <£) <£> 1^

jvel A q: o o o o o o o o o o o

1 M *-> o o o o o o o o o o o

3 o CM 00 o CM 00 o

_o o o o o o I—

1

I—

1

r—

1

1—

1

I—

1

CN

_o o o o o o o o o o o o

"(5 "(5 o o o o o o o o o o o

U- u. o o o o o o o o o o o

A-12
>

c

o

'.—V

Q0^

OCD£.00

O

OOOO 'j2 =

Q.

ni

>.

raE

E

3

c

Oo

a

c E

o ir> IT) O CO o <-i o oo

IT) I—

I

• — > k- -M — 1 U V V

oc ex: en

OOOOOOOCTIVDOO OvO^'^OOLftCOCNPO Ov-HOi-iOOOOVOvO ir>vOvO<£>Lr)LO^CN»-H ooooooooo

V> V) W) v> U) Ul Ul o o CN CO o o o

<<<<<<<<<

0^ .S2

-o

nietc o <" w

S -i S

III ^"5

§ i iD

•o « E

I ^ <U V

-8 ^ E

rslOl'^Oi-ICNfOCDt^tDCN

r^cNvc>a>cot^rHCNir>ooT-i ^.voir»'*'<tco«r»CN'-ioo ooooooooooo

o.-icNco'i-ir><r>r^oo<T>o ooooooooooo

OOOOOOOOOOT-i

V (J --8

bO c

ra (0 ^2

l/> 0)bO O in Oi

nj

(n oo LT) o LD in LD LD LD

0) (J OO 1—

1

oo oo oo 00 00 00 oo 00 > 1^ hevel A a: o o o o o o o o o o O

•(-> o o o o o o o o o o O

3 o OJ oo o CN 00 o

_o o o o o o I—

1

i-H T—

1

I—

(

r-H CN _o o o o o o o o o o o o "(5 "(5 o o o o o o o o o o o

LL LL o o o o o o o o o o o

A-13
3

on

I +J3

o

1 1 1 1 o

<)>

> Lft

T—

(

<)> o

<;> o

.—

i

o ^

_o

TO u_ o

o

1 1 \k 1 ooo o O O

u

(J o (0 o LO

"+-> <U o 00

njE

<u3 o Ld .—1 o cr

4-J 3 "(5

"5 _u >

o

1 V) r-H in

Lati -o

ni

ent

um X <j ni o E

E 10 <+-O 3 <u (/)

ber

ueri

iber

NumI a

o

E

3

C eved: ^ant: i-> (U c E

~ea

-t-i +j V

k1 3 3 O 9) 0) 2 f- on

'£)ooor^'£)(rMLr>>>)-oo

LDLDLnLDLO'^OOlTM'—

I

0C30000000

W) I/) u)

in u) m

(J u (J O O CN OO o oo CN LO <<<<<<< <<

OC o

<u ca -c >

<U V £ E

bO

OH

OCN'^LOkOCN'-H.—(IDLDr^ Lr>i£)0O'—loor^^r^r^'—lo r>-h-aiCNLn^£5o<rsLricNO h-Lr>'>)->^rOCNCN^OOO OcboOOOOOCDCDO

ooooooooooo O-—ICNPO'^LTlvOf^OOa^O OOOOOOOOOOr-i

nj

<u>

O

c

O

'in 'o

pre oc

<u •4-* hO c

OJ >

><

in

<ut>0 m

CN 00 o o o o o

ni CO oo 00 CN CN CN CM CM u o CM 00 CN LO 1^ > V LO <o <

>

OC o o o o o o o o o o o

1

-t-> t-j o o o o o o o o o o o

3 3 o CN cx> o CN 00 o

_0 o o o o o 1—

i

>—

1

I—

1

I—

1

CM

_o o o o o o o o o o o o

CD ro o o o o o o o o o o o

U- Ll_ o o o o o o o o o o o

A-14
01>

O

3cn

0

C0

o (/) o Lf> I—

I

o ID o oo 1—

(

ni o O) J3 o o v£5 -o 3 If) I—

1

u o- 4J

"(5

V) _o t

V

>

CM o

tis -o

ra

v> n c

4) 1/5 E X 3

U

O

E -a

tun V)

0) of CO

Number of

Queri

1

number

eved:

rant: -i-> 0) c E

(0 tri _« Lr 3 3 V v

OH OH 01 OH

OOh-OOO'^'^OOCN <i50oor^<—loO'—i"^oo

LOLnLDLDLTl'Nl-COCN"—

I

ooooooooo

(/)(/)(/)

U) U) W) <J "J o o o

o o

Lft <<<<<<<<<

CH .52

OH

(J

-C >

5 ^

S o > .

4^ E

3

c£ E

OCMO^'^r^CTiOfOOOvO CNvOCN^-iOLOmT-HT-HO OOOlCN'a-LOt^-OCNh-CNO N-LOLO^OOCNCNi-IOOO ooooooooooo

ooooooooooo Oi-HCN(r)'i-LO>Oh~OOCTiO OOOOOOOOOOr-H bO c

ni ni ^2

U)

0)bO

njV- CM o oo Lf) oo

OO o oo 00 r>- CM CM > <u Lf) ID VO

evel

/\ a: o o O o o o o o o o o

1 4-* •!-> o o O o o o o o o o o

3 3 o CM 'd- oo o CM 00 o

_o o o o o o 1—

1

.—1 I—

1

1—

(

1—1 CM _o o o o o o o o o o o o o o o o o o o o o o o

o o o o o o o o o o o

A-15
3

on

'•-> nj

+->

raE

E

o

E

c c

O LOOO LO o o ^ Lf) 1—

I

O) TM <u 5 -SJ Jj

O) (U 0) q: a: q;

.12 3

U

rage c

o o O o o o (N I—

(

<u CC o

-a

ll/> CM o .—

(

CTi O LO t: 00

00 CN CN o o O) TO

where 1

Ave Prec <r) o

00o00oOOoOOoCNo

CNo

I—

(

o

o

o uoisi:

evani

.—1 CD

<u > 0) 0) Q.C

o

-J

+Jc

a>

I/) 10 v>

U)O ieved

l/> Ul U) <J <J u O O <L) E

in u <J u (J o O O -o

<j o O O O T3 -D -a

-t-j <i) do -D -a -D T3 o O o

o um 'u

<u W

O O Lf) O O o O o o

R-Pr c J

Q ID<

1—1 <1—

1

<

CN<OO<

r—

1

<

CN<

Lf) < <

docs the

men Exac

r^ocNcyiCNr^vor^aioo <J>OCT)CN00t^^lf)lf>OO OOr-100CT)^0<X>CNi-IOO LOOOCNi— lOOOOO cbcDocDocbooooo

ooooooooooo O'-HCNOO'^LOvOI^OOO^O cbooocDoocboO'-i

ni

4)>o

co

'(/) 'o

pre be

<u *J hO C

ni TO> <u><

0)

O'^'-HOOvOCNCNCNfNCN ooooLOLor^'^t'^f'^'sa-T^ CMO'OOOOLOvOOiXSiCVO

CNCOfO"?t-^'*'«t-'«;f'^';J-'^ oooooocboocbcb

ooooooooooo OCN>*^OOOOCN>*iX>000 00000<— ooooooooooo 1<—1<—li—li—(CN C>OCiC)000000<p

0C3C3CDCr>000cj0C3

A-16
>

O

d d ^

o d d oooo

|w —

1_ Q-

(Q M

to

reE

E

3

to

0)u

0

V

0)

O

oID

c E

O LO o>

CO VD o oo o i« I—

I

« TM « M — .' 0) a> <u

OOh-000>£><X30^'*

000000000

U) U) 1/1 V) O O CN CO

O O

O -T3 000o

CM LO .-H <<<<<<<<<

a:

t^rMoor^cocy>oooot-iPoo ojcNCD'^finr^Lor-.co'^o

1—ii—locNf^cor^ocri'^o r~-if)^'^cooocNOJ»-HOO 00000000000

00000000000

o-—icNoo'^LO'or'-oocyio C>OOOOOOOOOr-i

0]

i_

0)>0

c

0

'.S2 "0

10

a> u

Q. 0

-a

<u j-j bO c

nj re> oi 0) >< <u

0)bO

re CN 0 CN CO 0 0 0 0 0

re CN CN 'd- 0 CN ID CO CO LD LD LD LD LD > a> LO r«- l>-

evel

/\ a: 0 0 0 0 0 0 0 0 0 0 0

1 4-» •4-* 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN £> 00 0 CN VO 00 0

_o 0 0 0 0 0 I—

(

1—

(

I—

1

1—

1

<N _o 0 0 0 0 0 0 0 0 0 0 0

"re "re 0 0 0 0 0 0 0 0 0 0 0

Li- u_ 0 0 0 0 0 0 0 0 0 0 0

A-17
3

I -MO

TOO

4)

E

£

oLf>

.Si

c E

00 ID r>~ O) 00 CT> O)

CT) O VO

« >

«

'i: Jl'

4> 4> 4>

OOCOOOCNIDCNO^ CNOOPOOOCN-^CO^OI LO\or»-r«-oocoooo^cN

pr)<r)oocr)cr)<r5«>j.—ii—

I

cbdooooooo

</)(/) W) OT <-> Ul U) 10

O -O

""^ O Lft o o CM fO o o

CN Lf) <<<<<<<<<

a: .2 J,

nic £ c

o fo ^ > M > a>

<u 2:

"S o

4J

Q- I/)

' O

QC -o

E ^

c 5 E

en

cMLOl—iN-Lr)'!tr»-ir)0^i«o ooO'-iT-icN>-i'>rir>ocNO '^O'^CJlLDCJlCOOOLOi—lO lf)«*COOJCN^<--IOOOO ooooooooooo

ooooooooooo 0--HCN<r)'*m^or>-ooo>o 00000000001-i hO c

w O T-( M CO

l/l 0)bO

ni CN CO CN o o o o o o

(TJ vO IT) I—

H

vO VO O Oi I—

1

r-H CN CN CN CN CN > 0) CN LD VO VO VO <£>

;vel A q: o o o o cz> O O O o O o

1

=4-* o o o o o o O O o o o

3 3 o CN 'd- vO 00 o CN CO o

_o o o o o o I—

1

I—

1

>—

1

I—

I

i-H CN o o o o o o O o o o o

Fal

"(5 o o o o o o o o o o o

LL. o o o o o o o o o o

A-18
rau

01

3

<u

ce.

in

ni M

to

>^

raE

E

to

oLD

I-

.2

c E

OO IT) o> OO 00

o> »-i 0> O VO

<U c • « 5 «J i; J^ _|

4> v a>

oor-i<r)'*Lr><-ivDOO<N CNPOCOCOOOCOCNi—1<—

I

000000000

U> V> (/) u) o o o o CM CO

V) V) V)

(J u u 000 -o -xj -o 000

i-l ON lf> <<<<<<<<<

Q: .!2 Jj « q: o

C c

O «> ra :^ -f 5

'!=« E

3

c

(/> o

O

q; -S ^ £

.-icNLO'<d-cr)fO'^CMr».'*o ^O^O^OO^^COLOOO .—(vOt—ir^fMr>->-HvOfOt—10 uncr)cocNCNi-i<-ioooo 00000000000

00000000000 ot-HCNco'a;Lf)vor»-oo<7)0 oooooooooo.--i

S--8

bO C

ra ra

4>hO LO 0 10 0 p«- "a- ra

fc. ra OJ CM CN 0 0 0 0 0 V 0 00 t—

1

00 01 <^ > CM CO in in LD L« LD LD

evel h q: 0 0 0 0 0 0 C3 0 CD 0 0

—1

4-' 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN <£> 00 0 CN 00 0

_0 0 0 0 0 0 i-H <—

(

1 T—

1

CN _o 0 0 0 0 0 0 0 0 0 0 0

"ra "ra 0 0 0 0 0 0 0 0 0 0 0

u. u. 0 0 0 0 0 0 0 0 0 0 0

A-19
en.

3

(J 01

rau

>^E

E

3

O O

niE

o

3

ni

eo

z. °

c E

3 3

CT> on

00 CM oo

00

CT) (T) CN

<u c • 5 5 a3

'i: JIJ _J

<U HI OJ q: a:

ooooooixsvor- CNCN'^LO'sd-fSOOCNCT) a)'i-ojo*or>-oooLr)

ooooooooo

in in in

in in in in

<J O O

O -a -O ^ O LO

Lf> >—I I—

I

o o CN CO o o 001 in <<<<<<<<<

Q: .!2 J,

nic i; =

.— s

u "3;

4) O

= -

<J £1 E c:^ ^Q- w =^c

I 4> <U -g ^ E

r-ir>-<Nooi^oor*-t-Hr«-ooo OOOCOLf>0<£)<—IvOrHVOi—

I

^-'*'*00<nCNCN^<-IOO ooooooooooo

Oi-H(>j(r)"5}-Lr)<or«-oqo^o ooooooooooo

oocbooooooo>--i bO C

(0 ns

-H O tH C>) CO

Averages Recall

Level

out lout

Fall Fal

Lf>r«-a)'*oocNir)'*t^Lr>'«d- CNLrir^CT)vc>^cj)r^Lf)Oi-i oocNa><-r*.i-icr)Lnr*-a)0

CN'^';^L^>L^>vovC)vo^O<o^»- ooooooooooo

ooooooooooo OCN^vOOOOCN^^OOOO OOOOOi— OOOOOOOOOOO li—li—1<—li—ICM ooocpooooooo ooooooooooo

A.20
4)>

c

o

0)

'+J

_(/)

'+-> ni

-i-> in

niEE

3

I/)

o o

E

o

3

ra CD

o

hOVt-t <a

(J

I CM'5

<U 01

vo a>

1^ CN r«- 00 O) CN

CTi CO CO

<u c •

•

<u 5 «j

• — ^

i; Ji _!

O) 0) 0) q: o:: q:

t 3

1/1 L>

rage: c

o o O CO o Lf)

<u ct: O

-a o

!s2 oo CT> <o CD CO CO LO CN CO 00 CO o O I-H r-H vo m

where

CO 1

Ave Prec LOo o o CD o

CO

C3

CMCD

1—1 o

c>o uoisi: levani COo

0) > V

01 Q. _J

in<J

01

>+-o c 1/1 in l/> c >

0) U) in <J y u 0 o

1/! u <J o O o -a 'in £ <J o O O O -o "O -o

4-» 0) E

3

3

u

O

-D

T} -o -o o o o

o "o

0)

in o o LO O o o o o o *-» Q_

1

c: u Q <D ID i-H T—

(

CM CO CM

docs 0) c

0> < < < < < < < < < *•* E

X

LU

oor^i—lOh-Locoi—ir^iO"*

CNVOCNVO"—icOOCOvOi—IIO LOLOr-OvO<—l|^CNvOC3>.-H r^LO"st'>i-c<ococNCNj.-HOO C3C3dcbc)oc>c>C3cbci

ooooooooooo Ot-icNcO"5t-LOvor»-oocj>o ocaoociooocDOi-H O)hO C

n) ni

^ O ri !S CO

o>bO vo o CO CM CM LO CM CM CM

ta

ni o 00 CM vo O CO CO

0) (J OO VO r-H lO o CM LO OO > o> CO LO vo vo

5vel A o o o d d d d d d d d

_i

•*-» +J o o o o o o o o o o o

3 3 o CM vo CO o CN vo 00 o o o o o o I—

1

1—

1

I-H >—

1

T—

I

CM

_o o o o o o o O o o o O

15 "(5 o cp o <p o o o o o o

u. Ll- o o o d d d d d d d d

A-21
>

c

O

u

w

Q.

01

i-ioq<£)'^^cNo oo^oo o T-»a3vo'*CNo oooo 'w —

a.

to

m

E

E

to

o 4-> Lf) E

o4-> 3

ni

c E

N- <-l CN 00

00 O) CN CO CO

a> V 4)

OON-00^00<0 voooO'-iooa)^'*ift cooor^'d-oo.-i^vo

ooooooooo

o o <N CO o o oCM in <<<<<<<<<

0)

c 2 ?

W > 4)

(J -TT

0)

0 .1 « •Si:-?

o « E

CL I/) c

1 <J <u <o a: -8 ^ E

00'*'^vDT-icN.-ir^r*-voo

LftLf)r».ovo»-ir^<NvoCT>i-< r^if)"*«^<r)cocNCN'-ioo ooooooooooo

ooooooooooo Oi-icN<r)'*ir)*ot^ooa>o oooooooocbo'-i

ni

k.>

O

c

_o

'w

'o in

0)

k_ u

a. O

•J3

0) M

bO c

ni ni ^ >

>< V

Averages Recall

Level

out lout

Fall Fal

fSLDO^O^'—I^OLO'—I'i-vO —IOOCNCNOOO»0^00«S-V0 "^J-OOvOCNLOOiCM^LCr^-OO

ooooooooooo

ooooooooooo OCM"*VOOOOCN'*-VOCOO OOOOOrHi— OOOOOOOOOOO 1«—Ii-HtHCN

ooooooooooo

A-22
4-)

>^

TOE

E

13 CO

O o

TOE

o4->

4)

c E

01 2

o in CTi oo oI O O 00

Lf) >—

I

^ 4Jc •

•

aj TM 0) ^ Jl' V <U OJ Q£. C£. en

oo'^L^)'!^cN^oooLfto LOLniOLftLC'^fOOCNT—

I

ooooooooo

o o OJ CO o oo

<<<<<<<<<

0)

Q- in

' o

IX. -o

o

£

c

^-^oc»aiooLr)i£>cMo-)r^o oor^ooooooLnor^ocsii^ ir)^oo"*a)'5)-cO'-H';tooo r^LOLft'^eooocNCNr-HOO ooooooooooo

ooooooooooo o»-HCNcO'*Lriioi^ooaio oooooooooo>-i

>

Oc

o

'u

pre be

a) 4-) hO c

nj > <u><

0)

01bO r>- 00 Ol o CN

nj

nj o 1—

1

CN ro

0) u r—

1

LD h- N- > Ln r». r«. r>-

evel A o o O o o o o o o o o

1

-t-»

-»-> o o O o o o o o o o o

3 3 o CM oo o CN vO 00 o

_o o O o O o 1—

1

1—

(

T—

t

I—

1

1—

1

CN

_o

"(0 o o o O o o o o o o o o o o O o o o o o o o

U- Li- o o o O o o o o o o o

A-23
>u

c

o

OOVO^CNO oo^do o .-HOO^^CNO oooo *3> zz

Q.

M

(Q M

CO

>.m

E

E3

to

3S

c

(13

_0>

O LO CT) OO OJ OO O 00

LO I—

I

5 i 0) O) 0) ci:

OOOOPOvOlrti-HvO

LOLftLOLftlD'^COCSli—

I

ooooooooo

V> (A U)

I/) V> U) U) o o CN CO <<<<<<<<<

« ct:

3

o

o

-o « *; c >- c

o «> nj sis0) —^ « o = S -

0 « E

1. w

=

1 o „ £

a: -§ ^ £

ift'-iorsiiDPOoncNvo^cN

'*ooc>4^o.-iir)CTicoir»ooo t^Loin^^cncNCvii-ioo ooooooooooo

ooooooooooo

OOOOOOOOOOT-i

01 fi

bO C

(Q ni

(/) 4>hO

TO o o OO CN CN CN CN CN

cal CN CO CO CO CO CO CO

r- OO O) o> > r-- r«. N.

Level A o: O o o o o o o O o o o

M 4-* o o o o o o o O o o o

3 3 o CN <£> 00 o CN xt OO o

_o o o o o o I—

1

r-H <—< I—

1

i-H CN _o o o o o o o o O o o o

Fal

"55 o o o o o o o o o o o

u. o o o o o o o o o o o

A-24
_o o o i-H LD .Si CO oo

(0 o

E

0) o 3 IT) 1—1 o IX

•M3 "<5

I/I _o >

'J o

Statis

chaul'

merits

>» 3 k_ U

ra O E

E in 3 4)

ber

ueri

iber E

3

a<<- t

3 ed: 4-> c

o C > ni V

c E

"(5 tri >

V Lr 3 3 4> «> a: ^^ OH

OOOOOOOCMOO^O <£>VOOCN.—lOOLOi—

I

coa>oor>-if)OfOi-('*

ooooooooo

I/) U) U)

in in in lA O O CN CO o o

<<<<<<<<<

0) cm o

O

-o c >- c

o 4) ni

41

4)

^-^ (0 o

t= s; >- o .22 4j 5) i -Q "O « E

CL w £

O^i-HCO'^l-CNCNOOi-ICNOO

r-.^^cococN'-ii-HOOo ooooooooooo

ooooooooooo Or-icNco«*Lr)vDr^oocy>o OOOOOOOOOOt-I

TO

k.>

o

c

o

'.— 'u in

4) u

O. O

-a

9)hO c

(TJ ^ > 4) 4) ><

4)

V)

4)hO o CO 1^ CM CO I—

I

I-H r—l I-H (Q

ni CM o o r--

4) o to Lfi r>- r~- > 4) CO LO <

"53>

4>

o d o o O o o o o o

_l

4-» o o o o o O o o o o o

3 3 o CM vO oo o CM 00 o

_o o o o O o rH I—

(

T—

1

1—4 »—

1

CM _o o o o o o o o o o o o "(0 o o o o o o o o o o o

u. u_ o o o o o o o o o o o

A-25
3

OH

3

Ll_

rau

w

mE

E

3

3'^

CTi IT) 0> O 00

00 CO

CO O Lft

«i C

« 5 «

•5 i Jl'

0) a> a> q::

00l^00'£>00000

cno^oooovoocoLno ^fOforooococM"—I'-H ooooooooo

v> V) U)

u) v> ui V) O O O CN 00 o o

CN un <<<<<<<<<

cyi"*cor^oofM>-iooooo ovocTiLOCTicor^T—i^-^o '^IDOOCNvOCOO^kOCOOO vOrOCNCN^.—lOOOOO ooooooooooo

O'-ieNC0'*ir>vor--00CTio ooooooooooo

OOOOOOOOOO'-i

>

O

c

o

'wj

'(J in

0) u

o. O

V 4-> bO c

(n > v

><

0)

^ O T-l CO

in01ho co <—

1

CO o 00 rH 1—1 1—

(

I—

1

t—

1

m

nj I—

I

LD CN CO CN CN OJ CN OJ

(J o CO 1—1 CN CN CN CN CN > lU CM CO >=t LO If) Lft Lft LD LO

5vel A o o o o o o o C3 o o o

—JM +-> o o o o o o o o o o o

3 3 o >£> oo o OJ vO 00 o

_o o o o o o r-H 1—4 >—

<

r-H .—

1

CN _o o o o o o o o o o o o "(5 "(i o o o o o o o o o o o

LL LL o o o o o o o o o o o

A-26
3

nio

O

r-H o

o

1—

(

^ X

- -4-» O 3o

ino

(0u

Of

"M

.!2 '+3

<n to

E

E

3

to

u o

EoM

3

c E

3 3

CT) LO Lrt ^ 00 r>- o>

r~- o Lft

« c • > ra

+-• 4) 4) — > k_ _|

01 V a> a: cn cm

OOOOOO^Oh-CT>Lr) vDOOCNOOOiCNOO^O^

ooooooooo

ui u) u) ui u (J <J o o CN CO o o

CM LO <<<<<<<<<

5

s: >

V

- 4)

I o

E

4) 4) :5 E

<T>Lf)cx5rsit^cy)OoooT—lo oocMr~-CN^Dcor-^oo h-TrcOOJCN^'-HOOOO ooooooooooo

ooooooooooo o^CMcO'^iovor^-ooCTio OOOOOOOOOOi-i

(S

4)>

o

C

o

'in

preci

OCS

4J +-> hO c

nj > 0} V >< <u

\

1 1

in4)hO oo r—

1

oo oo oo oo 00

(TJ CM h- ir> I—

1

h- h4> oo CO > 0) CO >^ LO in LO LO LO LO LO

svel

/\ q: O d o CD o o O o d> d>

1 -M o o o O o O o O o o o

3 3 o CM CO O CM oo o _o o o o o o ( r-l T—

1

>—

<

CM _o o o o o o o O o o o O

~m o o o o o o o o o o o

u- u_ o o o o o o o o o o o

A-27
3

o

.12 M

>,

(0E

E

3

o

c E

o in CT) O 00 CO o r»- ^ o r«- LO '—I

« 5 oi i; _|

4) a> 4> ai an Qi:

OOPOOOO'^^OOO

LOLDLniO'^'^fOCNr—

{

ooooooooo

M u) in u) <-> LO LO

O -O

"° O Lft o o

o o

<<<<<<<<<

^ .S2

q: ^ E

a:

oooooN.CN<-ii-H'<d-r>-a)0 CNCT)r»-CNOOLor^^oor>-o OCNLOO'^CTiCNLDOOCOO ooLO'^r^cocrMCN^ooo ooooooooooo

ooooooooooo O"—iCNcO'^Lnvor^ooCTio oooooooooo>--i

ni

0)>

O

c

O

'in 'u in

<u o

a. O

-o

<u M

hO c mk. > (1) (U ><

O .H CM CO

CO

<u

fciO <—

1

CN o CN 00 CN CN CN CM CN

TO

TO CO o I—

(

CO 1^ ID Oi a> O CO I—

1

vO CD 1 I—

(

T—t >—

1

I—

1

> <U CO LO \o r*- r- r~

jvel A QC o o o o o o o o o o o

1 4-' M o o o o o o o o o o o

3 3 o CN oo o CN 00 o

_o o o o o o I—

1

I—) I—

1

I—

I

I—

1

CN

_o

"(5 o o o o o o O o o o O o o o o cz> o o o o o o

u. LL. o o o o o o o o o cz> o

A-28
o (A o Lft ID oo

ni 1^ E o 3 r—

1

o cr

+-»

ns "(5

"5

_o > \ O

tatis

itri2- ents

u~> u

E3

^ O

ni O

E TO

um

U)V

M-o

(/)

ber

ueri

iber

Num a

o

b3

C eved: 'ant: •<-> c E •M

tri 1 3 3 ti ^2 01

oooooo'Nrvocx3a> ooocoooLOOLnoi vOCNT-tOCT>COvOCO">h

Lf>LOLOLO'^-'d-(r)OI>—

I

ooooooooo

(/) ui to

V) V> </) U) o o CN CO o o o o CN in <<<<<<<<<

5 cc: o

-C > — 4) 5 -a ^

O Si u « E

I ^ 4) <U oi: -g -5 E

LftCOi—lOOCNODOi—iLOr^O) 00<r)VOOLOCT>fOvC)OLf>0 r~.Lf>'*'<*<r)CNCM--i<-ioo ooooooooooo

o o o o O "-I CM CO o o o o o o o Lo <c r~- oo CD o OOOOOOOOOOt-l

V (J

bO c

fli (Q

o cr> vo ^ o

OCOt-HVOOCNCNCNCNCNCSI

ooooooooooo

ooooooooooo O<N'<d-vO00OCN^v000O OOOOO'— OOOOOOOOOOO li—li—IrHi—ICM

A-29
CNo

<>

.52

CD

>»

niEE

3

u o

(TJ E

o

i-> 3

I -a

v_U

O)bO

EOT

e E 3 3

a: z

O LO o CO COO O in i-H

0) *^ > re -Si 0)0)0)

01 a:

cc: .—

1 3

[/I U

rage: c

o o o o CO CN •£> cu o

](/) CO LD >—

1

LD r>- r-H ra

r-H oo ID CN CO 00 CN

where

c» 1

Ave Prec o o o o O o

CNo

TO

i-H o uoisi:

evani CNo

0) 0) > <t) w

Q. -J of

too

ed c

V

u> u> </) c >

4)

V) in in 10 u o o O o <U E

u> u (J (J u O O O -a '</>

' w u O o O O -a "O TJ <U

cu do -o -o T3 -o o o O

o k_ um 'o

10 o o ID O o o o o o al

1 a:

c Q Exact ID< <

I—

1

<

CN<CO<

I—

{

<

CN<LD<

r-H <

docs <u

4-*

men

on

r-cococNcocT)ioa^O'-ioo

5tC0LD.-IO">l-CT)Cr)Cy)0000 O^h-^LDO'^Oi'XSCOO ^-•<*cococncnt—loooo eioooooocboocb

ooooooooooo 0<-iOJfO'^lDir)N-OOCJ>0 OOOOOOOOOO—l 0) 4-> hO c

re re

0) £

10

0)hO I—

(

h- a> o oo -a- re

re CM ID oo ID vo r-H I—

1

T—

1

0) <J CN CM CD OO o> o o o o o > 4) CO "=d- ID ID ID vo vo vo vo vo

evel A q; o o o O o o o o o o o

1 -M o o o o o o o o o o o

3 3 o CN vo 00 o CN vo 00 o o o o O o .—1 I—

(

r-H I—

1

CN jO o o o o o o o o o o o

"re "re o o o o o o o o o o o

Li- a. o o o o o o o o o o o

A-30
>

3

c

o

' wy

<u

u

o

0)

ni to

raE

E

3

to ECr

5 o

^ in

CN OO "-I vo <r> O LO CO i-H

5 5 2

4> a> v

q::

OOOOCNOOOCN^OCM OJOOOO^O«;fvOOOO'-H "OLftLOLDLft^POCNi—

I

ooooooooo

in u) V) v) O O o

O "O -a

''^ O IT) LD 1-H »-l o o CM OO

o

-oo o o

CM Lft <<<<<<<<<

C 1- c

o ro>

o^oo^oN-iocNvoLncoo '*r»-cTiCTir>-ocNvocoi-HO 00lf)'4;P0CSCN'--iOOOO ooooooocziooo

ooooooooooo 0'-icNco«*ir><or^ooo)0 OOOOOOOOOOi-i hO C

ni ni

01bO CN CM CM CN CN CM CN CN m

nj CO I—

(

.—1 I—

1

t—

1

.—

1

T—l I—

1

<u o CM LD LO LO Ln LD LT) LO > oo LO LO LT) LD ir> LO

evel A d d d d d d d d d d d

_i

-(-* o o o o o o o o o o o o <a- 00 o CN vr> 00 o

_o o O o o o I—

1

r—

1

I—

1

I—

1

I—

1

CN _o o o o o o o o o o o O "(5 o o o o o o o o o o o

LL o o o o o o o o o o o

A-31
oo^oo o dooo

|S —

w

_o

'-(-> _S2 '+j

nj to

>.

IT] EE

3

to

o

c E

O ID CO

00 O o r»- o O O 00

LO 1—

I

>

=

4> a> 0) q:

ooooor»-oo>£>'«a- ovocoo'*a>ooor-i OOOh-'£)^vOOi£)iO

vDLr)LOLftLO'*">rcN'—

I

ooooooooo

I/) U) W) u) in V) in O O CN CO o o oCsl LO <<<<<<<•<<

5 cn

Z c

' o

Cd -a

§3

';)-or^oo'4-ooooo>oooo i.DO'iJO'^'—iCO'-H'-Hi—lO O^h^CNOOCNOOO^'^lDO r>-Lr)iD';j-'«j-cococs>-HOO oooooooooczio

ooooooooooo o^<Nco'*ir)\or«-coc?)0 oooooooooo^

TO

>

O

c

O

'5) 'o

0)

ino

Q. O

-O V •w bO c

TO TO ^ >

JV >< V

— 00

\

1 1

O

\V\C3

I/) 0)hO 00 <y> 00 CN CO CD cr> CO CO

TO^ TO to o CN Lf> CN <o y o <£> CN ^^ > t> <o

ivel A Ql o o O O CD o CD d CD o o

_i o o o o O o O o O o o

3 o CM 00 o CN 00 o

_o o o o o o r—

1

I-H I—

1

.—1 I—

1

CN _o o o o o o o o o o o o

"to "to o o o o o o o o o o o

U- u. o o o o CS o o d o o o

A-32
3

c

o

"55

3

OH

u

01 or:

.—

*-M

ni

-i->

>»

k_njE

E

3

to

y o '*j if>

niEo

-M3

TO

c E

3 3

01 z

O Lf) LO O 00 o r~- o O 00

Lft t—

I

i > £

'ii Ji. Jj

a> <u o) q; q; a:

OOCOOCOOJOiOOOO

LftL«LriLftLft«*POCN<—

I

cioooooooo

in in in u)

in in in

(JO O O CM eo

o

-ao o oCM LO <<<<<<<<<

O

-o

0)

w C

<U TO C > ^ 0)

"G 4J E ^

Sli = -IT

I <u S

-g :£ E

"^-CTlCN'^fN'-H.-IOOCNOOO

<—icocNh-cMh-.-i<r)t«-)Lr)o OOLDLO'^'^OOOOCNt-iOO ooooooooooo

ooooooooooo Or-HCNCO'^-Lovot^oocrio OOOOOOOOOO'-H

TO

(U>

oC

o

'u in

pre be

<u 4-> hO c

TO TO

k. >

><

in

(UbO CT> o CO o r*- r«- r*-

TO

TO

1—

1

ai CO o CM Lft ir> Lf) Lf) O Oi CM LD >- > 0) r~-

evel A ce o o o o o o O o o o O

1 -M o o o o o o o o o o o

3 3 o CM 00 o CM oo o _o o o o o o 1—

1

I—

1

T—l 1—

1

CM _o o o o o o o o o o o O

"to "to o o o o o o o o o o O

U- LL o o o o o o o o o o O

A-33
3

a:

(9U

01

v>o

.!2

«i

+-•

E

E

=) to

<-> o

niE

o•!-> m

n 3

c E

3 3

O LT) O 00 If) oo o r»-

Lf) >—

I

-a

<u c • « ra 4)

'i: ^ Jj

0) 01 4J ct:

OOt^OOvOOCNi-H

LOlOLOLDlft'S-COCNi—

I

ooooooooo

V> u) (O u)

U) (0 v>

O O CN CO o o

CM ID <<<<<<<<<

1- (J

»; q: o

(0

oj *; c 1- c

O K nj>

.52 5 0)

o>

E

3

01

a: -o ^ E

Lf>Lf)r«-OLOoieovDOLr)'—

r^-LO'^-'^i-cocNCN^'—lOO ooooooooooo

ooooooooooo Ot-iCNPO'^LOvOr^-OOCTiO oooooooooo---i

>

0c

o

'(/) eci V)o

P' O

-O

0)bO c

ni nj ^ > 0) 0) >< 0)

(0

0)bO 00 I—

1

00 o in 00 CO CO 00 00 (Q

k. fO I—

1

o 00 OO 00 OO OO

0) o r—

1

o \o CM CN CN CN cs CN > 0) IT) vO r>-- r>-

evel A o o o o o o C3 O d> d> o

—1 o o o o o o O o o o o

3 3 o CN vO 00 o CN VO OO o _o o o o o o I—

1

I—

1

I—

1

<—

1

I—

1

CN _o o o o o o o o o o o o

"ii "Jo o o o o o o o o o o o

LL. U- o o o o o o C3 o d> C3 o

A-34
niE

E

3

o o

raE

o

-i-> 3

c E

3 3

O IT) ^ OO vO o r*- O O IT) t-H

« c •

• 0) TM 4J

• —

J= _l

0) 4) 4) q:

vOvOOvOO<T)00'*CTi

LOLOLOLf)lD«*00CN<-H ooooooooo

(/)(/></>

(/> V) V) 10 o o

o o

CN Lft <<<<<<<<<

c >- c: O 41 nj

0) S:

4)E ^

oi -S ^ E

CNvOr>-Ol«i-l«*OCNOlO vOLDr^T—iif)ocor»-t-Hvo<—

I

h-Lr>'^«srcococN>-i<-ioo ooooooooooo

0'-HCNfO';i-Lf)v£>r^ooo>o ooooooooooo

OOOOOOOOOOt-i lU (-> hO C

m (0

4> u .1^

\ 1 1 1 1

_\ \4\

a>bO O oo ^~ oo oo oo oo oo oo m

<0 PO o

<u u t—

I

o CN CN CN CM CN > 0) VO 1^

avel ^ a: o o o o o o o o o o o

—i

-M o o o o o o o o o o o

3 3 o CN vO 00 o CN 'd- oo o

_o o O o O o T—

1

1—1 1—

1

1—1 CN

_o o o o o o o o o o o o

"to o o o o o o o o o o o

LL UL o o o o o o o o o o o

A-35
3

c

o

(DU

V

OH

3

1 1 1 1 O

< >

> T—

1

<

\

/ o

< > o

I—

1

^ X

_o

"(5 LlO

O

1 1 \sl 1 oo o

n

u

10 '+J

(TJ E

E

ISc

(TJ

(U <U

c E

3 3

o in o oo r>- o <x> o

^ c •

'i: ^1

O) V 0) OH OH CHL

OOOOOOLOOLO OOO^POCMCT)POOOOO OOCNCNt-hOO^-OO'S-LD

ooooooooo

U) (O (O

U1 (O 10 1/1 woo O o in

ir> I—( T-H o o CM 00

(Jo

-ooo

o o oCN IT) <<<<<<<<< a: -S :£ E

CT>i—iocT>coh-ir)Lr>o<oo

OOOLC'^COPOCN^OOO ooooooooooo

ooooooooooo Or-ioj<r)'*ir)<£>r»-ooaio ooooooooooo bO C

(0 CO

(0

t)W) O o 'St

ro m >£> st- I-H I—

1

I-H 1—1

(1) u oo I—

(

cr> "St > V r>- havel A OH o o o o o o o o o O o

_j

•*-> o o o o o o o o o o o

3 3 o CN <r> 00 o CN <£) oo o

_o o o o o o >—

(

I-H T-H 1—1 CN

_o o o o o o o o o o o o

"oo "to o o o o o o o o o o o

u. Ll- o o o o o o o o o o o

A-36
OJ

4-'

>.

(nE

E

3

(/)

a

E

<=y

c E

3 3

O ID O 00 CTi oO O

S 5 « i ^ _l « 4> a> (i: a: q;

ooooooooo

U) Ul 10 tn u) t/1 I/) O O CN 00 o oo CM IT) <<<<<<<<<

TOc ^ c

O 0) ro - -c >

S2 a <u

41 S: —^ 41

oi -§ ^ E

c

0 r—

1

CM 0 00 r- ir> '55 LD I-H eo irs 0 rsi LD IT) (TJ (0

4)bO 'y 00 Lf) 01 <r) 00 i-H 1^ 0 PO

4J f5 CN CM >—

1

I—

1

0 0 0 k.

4} CN

(0 0 0 0 0 0 0 0 d d > d

V

Q- 0 >< ion -0

"43 .52 V >

'0

41 4> (J _J

"(5u 0 0 0 0 0 0 0 0 Q. 0

-0 od 0 I—

1

CO vO 00 0 ~m

Re.

4) 4) u

u 0 0 d 0 0 0 0 0 d >—

(

bO

TO

an int OH > I V

><

rele uou

1/1 4)bO 0 vO PO 0 PO PO CO PO PO

ni CN 0 0 0 lO

4) (J PO If) CT> vO r>- > 4> PO LO in

evel

/\ d d d d d d d d d d d

—1 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN <£) 00 0 CN '3- >i5 00 0

_o 0 0 0 0 0 I-H 1—

(

1—

r

I—

1

I-H CM

_o 0 0 0 0 0 0 0 0 0

"(5 0 0 0 0 0 0 0 0 0

Li. U- 0 0 0 0 0 0 0 0 0 0 0

A-37
>

C

O

V

3

u

(0o

(0u

4)

E

E

3

o

I CM

in ac

_4J

c E

3 3

O If) 00

OO 00 o r>- r>- O O 'vO

LT) I—

I

- c

n> .r > j: i: Ji

0) 0) 0)

ooooeooo^ocNoo OOOOCMiOO^^-LOOLO oooaicT)^-'—icNi—IPO ooooooooo

(/)(/)(/)

10 (/) V) v> O O O

O "O "O ° o ir.

Lf> >-H I—

I

O O CM tr> o oo CN in <<<<<<<<<

—^ 0) o o .1 S3 o 4J E

CsL -6

<u a> £ E

OJLO-—l^-CMr-IOOO^-LCLf) OO^C0'*00»-lh-'^lO'd- CNO'^r^-ojvoofoai'^o N-Lft'^POOOCMOJ^OOO oooooooocbcio

OrHCMco>!d-Lr>vDr^ooo^o ooooooooooo

OOOOOOOOOO'-i

01 o

ni CD

V)

0)bO r— I—

1

o CO I—

1

(0 o o o o o

i_ ni CO LO a> in CO VO VO VO VO VO

f>- 00 v£> CM VO O) 01 O) O) o> O) > co in vO vp VO VO VO VO VO VO

Bvel h d o o o d d d d d d d

1 <->

-*-> o o o o o o o o o o o

3 3 o CN VO 00 o CM VO OO o _o o o o o o r-l I—

1

t—

1

i-H CM _o o o o o o o o o o o O "(5 "to cp o o o o o o o o o o u. o o o d d d d d d d d

A-38
CMO

LDo

3

0^

ooo o

oo

O 3

o

o

n

u

OH

(TJ +->

inE

E

3

c E

3 3

O LD CO 00 CN o 1^ OO O O PO

ID <—

I

4J c ••

4) TM 0)

'i; Jj

O) (U <u q:

oooor^CN'^OLO

'*^OOOOOOfMr-H>-HO ooooooooo

(/)(/)(/)

l/> V) (/> U) o o

oj CO o o

<<<<<<<<<

5

.2^2 U1 > <u

lU z.

^"— OJ o 0 J 4>

Q. .0

1 <J <U O) -g ^ E

000^00<OLf)00<£)0>^--H '£>r^ocovocNrsi'^oco'* OlCNPOs|•00>;^OOC^ICNl—lO LOPOCNrHOOOOOOO ooooooooooo

o>— ooooooooooo icN<r)'^Lf)vor-.ooCT>o 0000000000>-i

03

0)>

O

c

o

'wi

"(Jv (J Q. O

v -M hO c

ni fa> v <i> ><

4J

V)

<UhO CO T—

1

vO <£) LO LO LO LO LO

ro

k. nj o CO I—

1

o v£> CO CO CO CO CO

<_) T—

)

Lf) oo o I—

1

CO CO CO CO CO > a> CN CN CM CO CO CO CO CO CO CO CO

ivel A o o o o o o o o o o o

1 o o o o o o o o o o o

3 3 o CN vO 00 o CN oo o o o o o o <—

1

1—

1

1-H .—

(

T—

(

CN o o o o o o o o o o o

"(5 o o o o o o o o o o o

Ll_ LI- o o o o o o o o o o o

A-39
3

o

I o

COu

(« (/)

niE

E

3

3

C

E

V 4) E

c E

3 3

O Lf) I—

I

00 o in O O CO

Lf) rH

4> V V

q: Q:: q:

OOl^OfOOOvOvOCN

rNiooo^OLr>>-HLr)oo<-i

CNfNCNCMCNCM^i-HO ooooooooo

U) VI V)

u) 10 V) w) O LT) O O CN CO

o o O O

CN ID <<<<<<<<<

c 2 c

UJ > 4)

(0 ^ ^2:

^— V o

c

o

(/) 4-1 w 0) « E

0)

>- Q. w

a: -g ^

OOfOr^CNlD^OOO^CNI^i-l ^0'r-IOOh-OCNvO'3-r«-CO t>.vOOOCNOOLncO'-l<-lOO

ooooodooooo

ooooooooooo Oi-HCNcn^LO^or-oqaiC) ciooocziooooo^

0)>

oc

o

'55 "9

i3 ^-g

<U 4-> bO c

ni ni

t-H O « to

Averages Recall

Level

3

o

-*-» 3

Fall Fal

<-icNot^^-'4-cococopocr) lOOvO'^OOvOCNCNCNCSCN CX>^OOi-tCOlf>'OVD<£)<£)vO CMCNCOCOCOOOCOCOCOCO OOOOOOOOOOO

ooooooooooo OCN'^vC)OOOCN'«a-vDOOO OOOOOi-Hi— OOOOOOOOOOO li-trH<—ICN ooooooocbooo

A-40
>

C

o

'UV

Q.

a:

nj

E

E

2 ° c E

3 3

q: -z.

<N IT) O 00 Oi O O CN lO i-H

^ 4J

*> C

0) 5 4J

• — k- i: _J

0) 4> 1> cc a q:

OOt^OfOCNI'-H'^OO

COt—lO-—lOCTiOO'OLO ,-H.-li-(.-l.-HOOOO ooooooooo

«0 u) M u) O LT) LO ^ .—

I

o o CM 00 <<<<<<<<<

q: .52 ij

ni2^2 in > 4) u O- "ai

I "

-g ^ E

c

or^'^'-Hr».t^c»CMr<-oo ^h-h-O'-HvOPO'S-OOOO CNOOOOOOOOOO ooooooooooo

ooooooooooo

OOOOOOOOOO"—

I

bO C

4) 5

< ~t

1/1 <uhO CO o CO CO o CO I-H I-H I-H r—

I

I-H

nj

k. CO o o I-H r>- N- r*- t>- m (J I-H Oi I-H CN CM (TM CN > o I-H I-H I-H I-H fN CN CM CN CN CN

;vel A q: o o o o o o O o o o O

-J o o o o o o o o o o o

3 3 o <N 00 o CM vO oo o

_o o o o o o I-H I-H I-H I-H <-H CN 0 o o o o o o O o o o o

Fal

"(5 o o o o o o O o o o

LL. o o d d d d d d d d d

A.41
3

(JV

3

u

.—

03

-l-> to

raE

E

3

C

JO ^

2. O c E

3 3

00 00 o CO

oor— o<r>oo<r)^<i5 CNCNOOh-OOOOLOLO LDOOCNCNOvOPOCTlvO CNCNCMCNOI'—I-—100 000000000

W) W) W)

(/> U> I/) U1 000 O "O "O

"° O ID o o CM CO r-H CM L« r-l <<<<<<<<<

a: .!£ jj

«i q; o

(0_ c O <U (tJ -c: > (rt > <u

'<-> <^ «)

S o

> .c

^ E

3

c

I o

Q: -o

bO

o:

OOOlOLOOCOLDLftCOOO CNOOOO'^'^'d-'^f^OOOOO rMOOCOOOOvD>*CO<-lOO «;trH.-Hr-IOOOOOOO ooooooooocio

00000000000 Oi—ICNCO'^LOVOI^OOOIO OOOOOOOOOOt-h hO C

nj nj ^2

q:

"*'!j-h-cor>^"*ooooo cM^^ocoior^oooooooooo .-HT-HCNCNOJCslCNCNrslCNCN 00000000000

00000000000 OCN'*<^000«N'4-0000 OOOOOi— 00000000000 li—Ir-(1—ii—ICN 00000000000 00000000000

A-42
<-hOOvO'<S-CNO OO^-OO o i-hOOvO^CNO oooo—

>l

atE

E

3

O) <1>

c E

O Oi ^ O OJ o O <T> CM o <n <r>

IT)

" c •

"i: Jj

lU 0) <u q: ct:

OOfOOfOOOvOO^

0000<TivOC0OCN<—ivO

ooooooooo

<n (/) i/> (/) u

in (/>(/) y

ooooo o

OJ M Ol <<<<<<<<<

^ .!2

TOc =

C, (U W > OJ —' <o o

0 .i Si

a. =

1 <^ 4) <U a: -8 :5 E

bO

^DLDCNUOi-HON-'a-vOOOfO CM^-CTiOOvOi—i'£>00'vt-CO^O .-HOCNOOOOfN^-fvlvOCyir-i OOvOLTi'^rOCOCNCNi—lOO OOOOOOOOOOO

OOOOOOOOOOO 0'-HCNPO'>*Lr>>£)r^ooa>o OOOOOOOOOOr-H bO c ^-5

in

<l) bO oo <£> VO oo

ni CO vO OO oo o 'a- v <j >£) oo vO r—

1

<£) 00 h- > V oo Lft vO

ivel A cr O o o o O o o o o o o

1 4-» o o o o O o o o o o o

3 3 o CNI '3- OO o CM <^ oo o

_o o o o o O r—t t—

1

1—

1

I—

1

I—

1

CM 0 o o o o O o o o o o o

Fal 15 o o o o O o o o o o o

u. o o o o O o o o o o o

A-43
rau

«

on

I 3

o

>.EE

3

,y o *> ir>

njE

o->-> 3

to 00

4J

z. °

c E

3 3

a: z

o a> a>

in O Oi o (T) PO

<u c • 4) TM 4)

• — > k_ Ji _!

a> 4) 4>

OOOOfo-OOON-OJ '*oocNLOo^OLftir>i-i

ooooooooo

o O O O U -D

""^ O If) LC >—I 1—

I

o o CN CO o o o o

<<<<<<<<<

4> /V

ot:

nj

(J O- 4)

4> J

I

V) ^

"u «

4) a. 10

0£. -xi

3

(J0

-oc

(0>

_«

4>

k_ >+-O

k_

4)E

3 U) C -t-» c

4) 4J M E

a:

'd-o«a-ooo^oooococT>moo ooocNir>cNa»*ooo<T><y> '*0000';l-^OrHOOCN^£>0 r^Lr>Lr>'a-cocNCN<-i'-ioo ooooooooooo

ooooooooooo o--^<^4co'*Lr>^ot^ooa)0 oooooooooo^

4)>

o

c

o

"55

il <»

4) o

4) *J bO C

O rH CS PS

(/> 4>bO ID LO CO oo ID 00 CN

(0 (0 '3- CN 00 I—

1

CO CO CO <o CJ> LD

4) u rsi <r> 00 CO oo I—

1

CO \r> oo o

> 4> fO Lf) ir> <£>

ivel A o o o o o o o o d d d

_j o o o o o o o o o o o

3 3 o rsi <* oo o CN <o oo o

o o o o o .-H I—I 1—1 1-< 1-4 CM

_o _o o o o o o o o o o o o "(5 o o o o o o o o o o o

U- U- o o o o o o o d d d d

A-44
>

C

O

'o

0)

o

41

n

u

0) a:

to

>»EE

to

c O

_o LO "M

(0 _3

ni>

V

c

0)N

o

<*- ^u

«> J3

-oV

00

>»o

bO

«>

+->IDU

1 I—

1

<*-

_<2u

3

U)

4) V 4) E ^

c E

3 3

01 z

CT) h- CN Tj- ^ CD O O CD CD

ID

»» C •

4) 5 4) — ^

i: -2! _J

4) 4> 4>

bO OOOOOO'S-fNOOOl 00CNCT>^\O00OlLf)O oocNoo^cnooo^ovo

000000000

(/> U) ut

U) U) u> v> o o CN CD o o

CN ir> <<<<<<<<<

o: .S2 J, k. (J 45 q; O

(0c !!i c

U) > 4)

4J O

0 .2 «

•5; i: -o

•G « E

1 o ? ?

a: -o ^ E

o:

inh-cx3CD«*cDa>LOi-i«i-ir)

CNLOi-Hi—lOOrHOOi-HLOVOO

t-i<yirsii-i'a-oocNo^cDr«-'-H OOlDlf>^CDCNCNi-t<-HOO 00C300000000

OrHCNCD'S-lDVOt»-;00C7)O 00000000000

<DOOOOOOOOO«-i

4) u

4> 4^ bO C

4! u

.1 «

4)bO LD CD T—l 00 <£> I—

1

CD 'S- 0 CN n ra CN 00 00 <y> <—

1

CD IT) VO CD

4> u >S- 0 00 CN VO 00 0

> 4> CD LT) in VO VO N.

ivel A o: 0 0 0 0 0 CD 0 0 0 CD 0

1 *j 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN < VO 00 0 CM VO 00 0

0 0 0 0 0 >—

(

1—1 1—

(

>—

1

I—

1

CN

_o 0 0 0 0 0 0 0 0 0 0 0

~m "ra 0 0 CD 0 0 0 0 CD 0 0

Li- LL. 0 0 0 C3 0 0 CD CD <b CD

A-45
od^oo o oooo "«/) m:

.12

'-4-» nj

4-* (/)>,

TOE

£

Jo

c E

00 in rH O 00 CN

.-H

ir> o

ii: o: CD::

OO'^OOOOOOOOOOlOO csioocMoocNr^>-Hoooo OOPOCOOOOOCNCNi—lO ooooooooo

l/> I/) to

«) U1 U> Ul <-> (J <J o o <N CO o o

<<<<<<<<<

en o

-a

-C >

<u>

(L) E

Q- (/>

Lt -o *J h

c

a:

uocNh-t^crjCNoooiooo

i£)r«-CNf>o>—i^<Tivoooo ocNincTi'^r^cooooo LOOOCNi—I.—lOOOOOO ooooooooooo

ooooooooooo 0'-<ojfr)"*Lft<oi^ooCT>o oooooooooo>--i

<u>

O

c

o

]u)

"(J in

<u

k. u

Q. O

"O

OJ -M hO c

>

><

<u

</)

<L) bO IT) CM o ai

<T3 nj CD LD m CM LO to o o o LO 00 1—1 00 -Si- > <u CM CO CO CO -si- '3-

evel A q; o o o o o o o o o o o

_i

-i-j o o o o o o o o o o o

3 3 o CN CO o VO oo o o o o o o <—

1

<—

1

I—

1

I—

1

.—

1

CM _o o o o o o o o o o o o

(0 (0 o o o o o o o o o o o

U- u. o o o o o o o o o o o

A-46
i-ioo^O'<s-rMo oo^oo o

.—lOovO'^cNo dooo !s2 =

"Jm

CO

>.aE

E

3

to

o o

niE

o<-•

S 0)

c E

ZJ 3

on z

O <—

I

O CO o O 00 O O <£> lO I—

I

4J c

« TM a> 'k. 4j *~| M — —.' 0) a> 4> on ai

ooi^o<r)*oif>'*o 0O'*vDCT>CO<-H0Oir>'£> rMO>oo<£>cr)<r>oO'-ion VOLOIOLOLO'^-COCNI'—

I

ooooooooo

(/)(/)(/)

(O (O U)

o o

o oo CN in <<<<<<<<<

.!2 J,

it! ^

c 2 c

O a> TO :i 1 s

= ?; >- 55 i: 0 s; E ^

CL w

=

-D •£ £

OOOOi-HO.-HO"!t<£>CJ>^000 OOCMOOOLf)r^<T>CM^O'* 000<—IC0CX3O'*00<M^O t>«.vDin«*copO(rN<-i.-ioo ooooooooooo

ooooooooooo Oi—icNcr)'^-Ln^ot^oocy>o oooooooooo^

>

oc

o

'w

"oV o

a. o

-a V

bO c

ns nj> V

><

1)

V

no o 00 m I—

(

o CO en oo 1—

(

0) u "a- o o CN 1^ cy> > LO ID LD <o

Level A o o o o o o o o o o o

-t->

•4-> o o o o o o o o o o o

3 3 o oo o CNI oo o

_o _o o o o o o f—

1

1—

1

I—

1

CM o o o o o o o o o o o

Fal "i5 o o o o o o o o o o o

u_ o o o o o o o o o o o

A-47
3

u

4)

E

E

on

oin

It)

c E

3 3

O O)

00 fO

--H o o IT) i-H

-o 4J

<u c • • 0) TM <u

a> <u 0) a: od

ooMOcO'^oO'-ir^

ooooooooo

i/> in I/) (/) O <J <J o o o CN CO o o

CM LO <<<<<<<<<

Q; .!2 i, i_ u

njO <u ni - 5

> . II

c:

I O

a: -o

0) <u £ E

1—i'«*CT>CT)Lr>r«-ooLoooo

ooooooooooo

ooooooooooo Or-tcNcO'd-Lftvor^oooio OOOOOOOOOOi-i bO C

ni (Q <

"5

<ubO h- CM o 00 o VO I-H m T—

1

nj

v_ TO en cr> CN LO <—

I

a> in VO oo O PO r«- o in VO 00 o r-l > 0) Ln in <o VO VO

svel A o o o o o o o o o o o

1 •M 4-> o o o o o o o o o o o

3 3 o CSI vO 00 o CN VO oo o

_o o o o o o I-H I—

1

.—

1

^ CM o o o o o o o o o o o

"to o o o o o o o o o o o

u_ o o o o o o o o o o o

A-48
>

QO Oj-OO o oooo !s2 =

a.

>>E

E

to

C

£

c E

CO CT> Tj- OO 00 "It ^ CN O O

-D 4J « C •

•

(U TM (U 5 Ji. Jj

4> 0) 0) (X CC

OOh-OOOOOOCJ> O'S-CNOOOOCNOO'* o^O'^cooooocricN VOLDLOmLDCOOn.—I-—

I

ooociooooo

(/)(/)(/>

<J o o

o -o -a

"^^ O LO

Lf> I—I r-( o o CM 00 o oCM LC <<<<<<<<<

DC .!2 5 q: o

x: >

' O

CC -D

E

c

-5 E

c

o r—i CN oo o 00 o oo LO is

CO o co o oo oo ni o I/) 4)bO

>£> IT) o 00 o t>- I—

(

.—

1

rec LO OO CN rvi I—

1

I—

1

o o i—

0) CN ^ o o o o o o o o o o o > o o >< ion -a

_ w 0)

-*-» > 'o nj V o

_J

"(5

t > o o o o o o o o o o o a. O od o 00 00 o

eca Re. o o o o o o o o o o t-l age ant

<uc

en >

><

rele non

<1) bO 00 00 o 00 LO

(13 (U oo 00 oo o oo CN

<U u i-H oo LO o 1—1 > <u CO oo LO LO LO LO

evel A en o o o o o o o o o o o

-J

-M o o o o o o o o o o o

3 3 o CM 00 o oo o

_o o o o o o >—

1

1—

(

CN

_0 o o o o o o o o o o o

"<i

"(5 o o o o o o o o o o o

u. U- o o o o o o o o o o o

A-49
3

01

to

.12

TO M

IT)

OJE

E

3

O O

njE

o3

OJ

c E

3 3

01 -z.

O Oi 'd- CO <£> o ^o r«-

C ••

(U ro 4)

• — ^ k. i; ^ _|

0) 4) O) 01

oooor«-0'*voco

00t^<£>LOCNCTiaiLOLO

ooooooooo

u) (/) to u) U O O

O "O "O o in

ID >-( I—

I

o o CN CO

u) vt u) u o o O O O

-a -D -o o o o o1—I CN LC <<<<<<<<<

— 3

01 nj -c >

V 4)

S b

> .

« E

I o ^

"

q: £ E

OOOCMiOOvOvOOOCOLDCN

CMOCN<oo>CN'a-'«d-Lri<r)o oqr^-vpLD'a-'^oricNt-ioo ooooooooooo

ooooooooooo O'-HCNPO'd-iO'or—ooa^o OOOOOOOOOOi-H

41 Xj

V 4-> bO C

CM .-I o

(/) 4)bO o I—

1

vO CO oo

ni

k_ n cs vO 00 00 ID CO oo oo u <—

1

CN o O CN VO > v IT) vO r~

svel A q: o O o o o o o o o o o

_i

•4-* o o o o o o o o o o o

3 3 o CM CO o CM 00 o o o o o o .—1 .—

(

I—

I

t—

1

CN _o o o o o o o O o o o o

Fal "To o o o o o o o o o o o

u. cz> d d d d d d d d d d

A-50
U

.!2

Rl

4-» (/)>l

niE

E

3

i-> in

ni

S 0) E

c E

O O) 00 o oo o O CO o o rLO t-H

T3 iJc • • > ra

2J >

*• i; Jj

a> 41 a> CSi

OOOOOVOTj-oCN

CNOOOvOCNi-lT-HlOlf)

ooooooooo

(/>(/>(/>

in in u> u> o ir> o o

<<<<<<<<<

w U

"J: -o ^ > (n > 4) " "55

4)

C in L in c

I y 4) «

-g ^ E

Lr)CNvo<ocNOOcNO>r»-r^<N

forsiLcoO'—imi«LOLf)voo OOf^vOLOLO^OOCNrHOO ooooooooooo

ooooooooooo Oi-lCNCO'<d-Lf)vpt>«-000^0 OOOOOOOOOOr-i

k_4>>

o

c

o

'in 'o in

4)

k. u

Q. O

-O

4> M bO c

ni ni> 4) 4) >< 4)

>

Q

<

oz

in

4)bO CN o CM oo CT> 00 CO

ni^ ni CT) o 00 «s- CO 00 00 00 VO

4> o CO »—

1

If) 00 CO Lf> VO > 4j »a- If) vO N- r«-

5vel A q: o o o o o o o o o o o

1

•4-*

•4-* o o o o o o o o o o o

3 3 o CN •«t VO CO o CM oo o

_o o o o o o t—

(

.—

1

»—

1

.—

1

.—

(

CN _o o o o o o o o o o o o

Fal

"55 o o o o o o o o CD CD o Li. o o d> d> CD <z> o d> ci d>

A-51
3

c

o

"in

o

on

3

rau

OH

•t->

E

E

CO

I

c E

en 2

O Oi CT> 00 PO o 'd- CO o o

(1> <U (U q; q;

bO oor--oO'<*r-'!j-oo

^DLOLr)LnLf)OOPOi-4'—

I

OOOOOOOOC)

U) ui V) O

LO 1—I i-H o o

(Jo

-oo o o

<<<<<<<<<

OH .!2

2i 4> I E

c

q; ^ E

a:

OLDLDOLDr^O^O"—I'-D'—

I

r^LO'd-'d-pOCNCN^.—lOO ooooooooooo

ooooooooooo O^CM00TfLf)^C>r~-00<T>O OOOOOOOOOO'-i

<U (J

bO c

ni nj

w

<u

tiO LD PO .—

1

nj CN o LO OO o

(TJ CTi <£> O) T—t o PO Lrt OO PO

(J CO CM r>- CN LT) Oi o OJ PO LO > a> OO Lf) Lf) in Lf)

jvel A O o o o o o o o o o o

1 O o o o o o o o o o o

3 3 O \0 00 o CN 00 o O o o o o »—

1

r-H t—

1

I—

1

CN _o O o o o o o o o o o o

Fal O o o o o o o o o o o

LL O o o o o o o o o o o

A-52
OO^OO o OOOO 'ut —

1— Qe

E

3

o o

E

o

c E

oO CO CO

<3- CN o o LD I—

I

>

=

S 5

V V V

ai Qd cd

OOOOOOOOOOh- ovO'iS'^LnoLD.—

vDvO^OyDLD'^-rOfNi—

I

ooooooooo

in to V)

u) </> u) (0 O O

<<<<<<<<<

q: o

c c

O 4) TO

.i2 g _a)

(TJ

5 O

> ,

C 4-1 in C <J <U 4)

Od

1—(.— ir^.—icNO^ooo v£3f<O0OC»OO.—it^Or^CNO^

ooooooooooo

ooooooooooo o<—icN<n'a-Lr)i£>^-00CT)O OOOOOOOOOOi—

i

4>>

O

c

O

"SG 'o in

ID u

Q. O

OJ •4-* bO c

CO > 0)><

\ 1

1 1 1

\\\

in

<ubO <-M 1—

1

OO o r>- 00

ni 00 OO LD o in

0) o OO '3- OO OO o .—

c

> 0) CO Lr> vO 1^

svel A Qd O o o o o o O o o O o

•4-1 -M O o o o o o O o o o o

3 3 O CN OO o CN '3- OO o

_o O o o O o r—

1

>—

1

1—

1

1 T—

(

CM _o O o o O o o O o o o o

Fal O o o O o o o o o o o

Li- O o o O o o o o o o o

A-53
3

o

V

a:

3

01u

on

CO

E

E

o

d 3

ECr

c E 3 3

OH z

o oo

CO

"sf o o

-a>

= 5 5

«> 4J OJ OH OH OH

oor~-oo<x3'*oo oooooooO'-HOOLri vDor)cnrooooo>LOLft

<0'X>>o^c><0'S-<r)CN»--i ooooooodo

V) (/)(/) (n V) U) (/) o O O

O -o -O O ID ID I—I ( o o CM CO o o

OJ ID <<<<<<<<<

OH

OOlDOOrOI^-i—iCMtDvOOOO h-<X>lDLD^'*<r)CN^OO ooooooooooo

o o o o O CN CO o o o o o o o LD <£> r*- oo cri o ooooooooooo bO C

.H O t-l M to

v>

01bO ro fO CO I—

I

o I-H CO mk_ ni oo I—

1

o o i>- CO r«- u a> CN 00 CO <£> <y) t-H CO ID <o > v (?0 ID ID

svel A o d d d d d d d d d d

_j o o o o o o o o o o o

3 3 o VO 00 o CM 00 o

_o o O o o o t—

1

i-H 1—

1

_o o o o o o o o o o o o

Fal U-

"(5 o o o o o o o o o o p

o d d d d d d d d d d

A-54
I

I—lOO'^'^CNO oo^oo o

I—lOO>£>'^<rNO oooo

CL

CO

>>n

E

E

3

CD

OID

a> <i)

z. °

c E

oO 00 00 o CO o r>-

c • • 4) ro

a> B Ji.

O) <u <u

Q0 0 0 0 00 0 I—

1

CN h- 00 00 a> LO CN LO l>- CM VO LO 00 'S- in in LO CN >—

1

0 0 0 0 0 0 0 0 0

U1 U> (/) U) 000 O "D -O O LD

iD '—t i—i o o CN PO o o o o CN ID <<<<<<<<<

O

-o

<u nj J= >

5 -Si

E3

o: -5

q:

q:

'^•CNoooovDOOi^Lrir>-oa) '£)OOt^CN'*OOCT>--i^Ol'5)- OOOPOO"*^-CT>';f'^LnO ^-'OLCLO'S-OOCNCN^OO oooocioooooo

00000000000 O-—ICNOO'J-LO'Ot^OOCTiO oooociooooor-i

10

a>hO

TO CN r-l '3- CN 00 0

nj >—

1

CN >—

1

0 CM LO 00

(J <7l 00 LO 00 lO 00 0 1—

1

CN 00 > (U '3- LO LO <o r~

Level

/\ CC 0 0 0 0 0 0 0 0 0 0 0

4-* 4-J 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN '3- 00 0 CM '3- \o 00 0

_o 0 0 0 0 0 I—

1

1—

1

.-H I—

1

I—

1

CN _o 0 0 0 0 0 0 0 0 0 0 0

Fal nj 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0

A-55
>u

c

o

'5!

4)

>u

(0u

o::

4> Q.

.S2

flS

-*-» CO

enE

E

o o t-i LO E

o

I fOoo

a

0)

eg^

c E

O CT) rH 00 o^ o o

(O TM (U 5 Jl' 0)0)0)

Qc: q: cc:

OOr^OPOCNCOLOCN

';^cooool^>«l•'^•<N^

ooooooooo

Ul V) u>

o o CN CD o oo CM ID <<<<<<<<<

a: .!2 J,

Rlc 2 ?

O " n> :2 -f s — o) o

O .2i 0) •55 i; -e n <u E ^

c

V 0)

CNlf>vOCNvOt^LftO<LOvOeN L0rHCT)^0^<N0»OC0<—ICN oo<r>CNr».«Nvoi^<r)N.Oi-i

ooooooooooo

ooooooooooo Oi-tCN<r)^ir)voh-.oqo>o OOOOOOOOOO'-i

0)>

o

c

o

'55

««

bO C

ni ni

4) 0)

to

0)bO oo o CN CO o <£> CO LD

(Q

k_ ni 00 I—

1

CN o oo CN Lft

4) u oo OO T—

1

CO LD O) o 1—1 > «) CO LT) vO r--

£vel A o d o O CD CD CD o O CD o

_i

-M o o o o O o O o O O o

3 3 o OJ «a- <£> 00 o CM <t OO o

_o o o o o o .—1 1—1 .—

1

i-H CN _o o o o o o o O o o o o

Fal o o o o o o o o o o CD

U- o o o o o <Z) o CD <b o O

A-56
3

0)

3

4>

m

u

to

TOE

E

3

IS

o

a

eO^

c E

3 3

O Oi VO O 00 00 O 00 O N.

-D iJ

4) c S 5 «

'i: ^ Jl' V a> 0) q:: Qi:

oooor--^r~-or^ ooooo^^r^^>£>r«-

O'OOLDLft'S-fOCNi-H ooocziooooo

(/) to (/> U) o O o

O "O "D

""^ O LO

ID 1—I .—

I

o o

o

-oo o o o

<<<<<<<<<

o ^ .52

C c

O 4* fO -i s

i5 —— so

o .!J S

ij « E

4J 3

Q_ w

^

<i: -5 ^

ct:

oor^"—I'S-oo'S-LTifM'*!—

I

OOLOCNCNIOCDLDCNvOLDLr) '^•a-oooooovoofn^-O'—

oouiJLft'ta-'a-POoocN'—i^-HO ooooooooooo

ooooooooooo O"—iCMC0'sd-Ln>£;r>-00C7io OOOOOOOOOO"—

i

bO c < 2

1 1

U1hO 1—

1

CO o LD CO CO LD

(n

ra LD CM oo CT) CM o t—

1

CO u CT) >£> o CO 00 o CO "3- > a> (T) LD r«- r<-. f>-

5vel A ce O o o o O o o o o o o

1 «-> o o o o O o o o o o o

3 3 o 'a- OO o CN 00 o

_o o o o o O f—

1

.—1 .—

1

rH T—l rsi _o o o o o O o o o o o o

nj "(5 o o o o o o o o o o o

Li- o o o o o o o o o o o

A-57
u

3

u

nj CO

>>

k.

(TJ E

E

EOT

c E

3 3

0 CTi <r> 00 00

01 •<^^ oo

CTi O Lft ^ --I

~9. ^

5 5 "S

r* ^

1) lU (U Od Od Ql

oo(r)oooopO'*r~-

<£)<OlDLf)Lr>'*00'—I-—

I

ooooooooo

</) (O V) uo

o -aW li^ o o CM CO

o

-T3 o

o

o

-oo

CM <<<<<<<<<

Od .!2

a: o

T3 _ c

O "3 - -C >

0) S o

> .

- i

r^cooor«-^^<Nio^ofo vOh-oooot^cftOPOirioofM OOOOO'—ICOVOCT)^'^.—lO N-LOLO'd-COCN^.—lOOO ooooooooooo

ooooooooooo o<-icN(r>'a-ir><o^-ooCT>o 0000000000--!

>

o

c

o

S2 'u

pre OC

lUhO c

nj^ >

><

1 1

4o

'-if»0'*'<-'-Hr~-CNOcocO'S- lOlt^LOCnOOLOvOI^LDO fNOOCNir>r>-o<-H<Nco'a-Lr) OOOO'^'d-'^LOLOLOLOLftLO ooooooooooo

ooooooooooo OCsl'^vD00OeN'<a-vD00O 00000<— OOOOOOOOOOO ii-Hi—1<—It—104 ooooooooooo OOOOOOOOOOO

A-58
»-IOO^O^<NO OOj-OO o •-HOOVO'd-CN oooo [to =

Q.

.—

**Jn4-» to

>s

niE

E3

to

o o 4-> IT)

TOE

o*J3

n>

1 s

c E

3 3

O CD O OO O o ^ O O VO

«> c •

• I >

«' j= ^'

<U O) u

q:

oor^ooovOLO.—

I

oo<>jvooo^a^aioo CNr-IOOLCrOOCvJOCN

ooooooooo

w «> 10 y

(/) U) U) U) o o CN CO o o

<<<<<<<<<

^ -g

S b

> ^

3

C

C

(U 4) Qi ^ E

oo'a-aDvo^ooopOLr)t>-CT>

or>~0(r)h-CT)'—ipOLOi—(O CXJLTiLD'a-OOCNCN'—lOOO ooooooooooo

ooooooooooo O"—icMoo'S-Lr)vor^ooCT>o oooooooooo.--i

>

O

c

O

'(/) 'u I/) pre OC

0)bO c

nj > A)>< 0)

(UhO oo LD OO LD OO '3- r«- o t (TJ OO CXD CO CM OO OO CN o 00

CM t—

(

CN ID 00 o T—

1

CN CN > <u OO 'd- LO LD LD LD

jvel A o o o o o o o o o o o

_i

*-> o o o o o o o o o o o

3 3 o CN 00 o OJ OO o

_o o o o o o .—1 1—

(

1—

1

.—

1

CN _o o o o o o o o o o o o

Fal (TJ o o o o o o o o o o o

U- o o o o o o o o o o o

A-59
>

3

u

c

o

'w

'<J

0) Q-

>%

njE

E

3

oID

c E

3 3

in o> p>-

IV. 00 vo O o O

<U V (1> OL en on

OON-OOOOiOOOO CNOOOOO>r>CT)(rOv£5<—

I

fOrHr-HfVJOOOOOOO<N

ooooooooo

W «) 1/1 I/) (O (/) (/) o o <N 00 o o

CN IT) <<<<<<<<<

5

c: c

<U TO c >

4)

-a>

Q- (/)

I "J

' O

-o

E

3

C

hO

a:

vD00CN'*'!3-OOCN<r)r^.-H

CSIkOi-nr^rHvOCT)'^'—ILDCM <o«*'*<r)(r)CNr-i.—I.—loo ooooooooooo

ooooooooooo

ooooooooooo—

I

lU (J

bO C

cs ^ o

OJbO 1^ CN PO CN

ni

k_ flj CN <o <x> CO oo CN u CT> CN oo CN <o oo o CN > 4> CM PO in in in in

svel A OL O o o o o o d> o o

1 •M O o o o o o o o o o o

3 3 O CN >£> C30 o CN VO 00 o

_o O O o o O I—

I

>—

1

I—

1

r—

1

.—

1

CN _o O o o o O o O o o o o "(5 ~m O o o o o o o o o o o

U- U. O o o o o o o o o o o

A-60
o

V

Qu

U

W

o

**Jn+j

c/)

niE

E

3

O O 4-1 m

E

o

S ft) E

c E

3 3

CN a> CM

CN OO i-H

'd- r- VO O Lf>

ft> c • « 5 «

4-> — ! 4) 4> 4> Q£

a: 1 3

tA U

rage c

o o o o r«- o o 00 CN ft) cm o

Precisil CN <£> 00 CM 00 <iin

vO

i-H CO CO CO CN o> <£> 1—4

where

' \r> 1

Ave

<r> o

COo

COoCOoCOo«NOCMO

r-l o

1—4 o cision

levani <Nc>

^—

ft) > w

ft) a.c

o

_l

f— u> M

U ieved

mei

I/I M U) U) u u O 0 ft)

</> u U u u O O O -a '.— 3

u o O o O -o -o -o 4) E U O

T3 -a -o -a -a o o o

o 'o

ft) 3 in O o If) o o o o o o ol C Q LO.-lr-ICNCO»HCNLr>»-l

<<<<<<<<< 1 a:

docs the

men Exac

Lf)0O0O'«J-<N'^'—lO'^OOCN oo<Nr>-vocovooo^ooir>o oooooovc><yiijocNr«-^<N ir)cococo<N»-ii-H^ooo oooooc>ooooo

ooooooooooo Oi-ic>Jco'S;if)vor>>-ooo^o dooooooooo<-i

^ i3

ft) 4J bO c

.1 es n

v>

ft) bOmk. co CO CO oo o CM

cal CO CN CN CO CN ID VO oo

4) CO CN r«- .—

1

r— I—

1

CO "t VO > 4) CN CO co •«* •«* IT) in

;

Level A q:: d d d d d d d d d d d

o o o o o o o o o o o

3 3 o CM ^ VO oo o CM «* VO oo o

_o o O o o o f—

1

.—

1

I—

1

CN _o o o o o o o O o o o o

Fal

"(5 c> o o o o o o o o o o

U- d d d d d d d d d d d

A-61
00 <r> CM o

o o J- o o

o

'in 'uV

CM

OO ^ CM O

C> O C) O

u

on

TO

TOE

E

OLO

EOT

c E

o a> CM O OO CM O IT) O VO

LO I—

I

a> a> o) q; q; q:

O0r-O000^~'-H<:t cMoooO'-iooo^(r)'-io

<—i^fOi-Hoor>-ooco vOLOLOLD^OOCOCMrH ooooooooo

I/) U) V) U) <-> U) U) I/)

Lft >-H I—

I

o o CM CO o o o o CM LO <<<<<<<<<

q; .!2 J,

COc c

O <U TO

!i5 ^"5

I 4) I E

a:

<T)i—iCNO^CMvDLO'^-r^LOOO

^oooot>-cM'>rpoh-'d-a>'d- LOCMcor^cM'srcD'^oocMO r^iO'S-cooocMi—i^ooo oociocicDcioooo

o o O --I oooooooo <r)«*LO>£)^-000)0 OOOOOOOOOOrH bO c

TO TO ^-2

(0

0)bO CM .—

H

o 00 LO LO CO <o I—

(

TO

TO PO <o LO <o CO CO CO CO LO

<J O o o 00 <£> OO o CN LO > V 'S- 'd- lO LO LO LO

svel A on O o o o o o o o o o o

*-> O o o o o o o o o o o

3 3 o CM <o 00 o CM <£> 00 o

_o o o o o o >—

1

r-H 1-H I—

1

CM

_o o o o o o o o o o o o

"to "to o o o o o o o o o o o

LI- o o o o o o o o o o o

A-62
niu

0) OH

3

O

ID

r—

t

o

o

O 3

O

O

niu

V

n

CO

>.a

E

E3

O o 4-< in

niE

o

-M3

(0

E

c E

3 3

O CTl LO 00 IT) ^ o o

Oi

c. S 5 «

.5 Ji Jj

O) 0) v

q: ci:

OOCOOO'*CN'a->-H

ooooooooo

l/> (/> u> U) (/> U) U) o o CM eo <<<<<<<<<

.12

0) c > — 0)

> .

LncN'^^^otr)a^o^^l^«-o

oo'£iLr)'<*'<d-cr)CMCN.-HOO 00000000000

00000000000 Ol-lCNco'^^lf)vor^ooCTlO 0000000000<-l O) 4-> bO c

n> ni

<[> V

<L> hO 00 CN CN CO

(D

nj 00 0 00 CTi r— u OJ 00 I—

(

h- 01 0 I—

1

> <u LD ID <o r«- r»-

svel A 0 0 0 0 0 0 0 0 0 0 0

_i 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN 'S- <£> 00 0 CN '3- 00 0 0 0 0 0 0 I—

1

1—

1

T-H <—

(

CN _o 0 0 0 0 0 0 0 0 0 0 0 "(5 "(5 0 0 0 0 0 0 0 0 0 0 0

LL. u_ 0 0 0 0 0 0 0 0 0 0 0

A-63
j-lOOvO'S-CMO i-lOOVO^a-CNO OOj-OO ooo o

o

|j2 =

Q.

>%

niE

E

13

<J o *J IT) m

E

o4-> 3

ni cd

c E

o ^ o O CT> O CO

<N r-4

IT)

« C • S 5 ti — w

i: Jl! _'

0) 4> OJ

OOCOOt>-CNO)0>00

COfOCslCNCN^OOO 000000000

"> y y y o o

O -a -o

"° o Lft I—

I

u u o o

O O -D 000O

CM CO .-H

(/> W) l/>

<<<<<<<<<

-c > —' a>

A) -Q

c

4> V

a:

Ot^COCSICOCN'S-LOLDCN'a- ococNr~vocor^a)0'<d-oo •sfCTi^lDCslOvCiCNCTi'^CN IfJCOCOCNCNCNi-HrHOOO 00000000000

00000000000 O'-tcNco^ifjvpr^oooio OOOOOOOOOOt-h V 4-> bO C

ni in

>

a

a

<;

oio

z

V

bO CO 0 <£> CM T-l 00 0 00 fli ^ ni 1—

(

IT) CM r<- CO CM 0 01

a> <j a> CO 00 0 1—1 CO vO > CM CO CO CO 'a- <

>

q: d 0 0 d d d d d d d d

_J

4-> 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN VO 00 0 CM ^ VO 00 0

_o 0 0 0 0 0 .—

t

«—

1

I—

1

r-l I—

1

OJ _o

"(5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

u. 0 0 0 d d d d d d d d

A-64
1—lOOvO'sfCNO OO^OO g T-(0O^'*CM oooo '.— —

0.

COm

E

E3

CO

(J o

m

E

o

3

to

nio

I CN

k_ '5c

c E

3 3

O 'd- o VO T-H O O VO O CM i-H

TO iJ

*> C

J 5 "S

O) <U 01 O) q:: o::

OOfOOOOOOOLDCSI

CNOiOCMh-N-OLDPO "^'^OOCOCMi—li-HOO ooooooooo

I/) W) (/) U) o

I/) I/) (/)

o o CN CO o o

CN LO <<<<<<<<<

5 a: c

4)

asc

° ^ >

!2 5 4J S o

> ,

« E

3

IDOLO.-ir^CM'^CNCNvOvO fOi-lOCOCDOOM'tOlDOO T^vOt>-Oi£>CN<T)Lf)0'*^ VO'^CDCOCNCM"—!>—I.—lOO ooooooooooo

ooooooooooo O<-HCN00<*Lr>vCi^-00CT)O OOOOOOOOOOr-i bO c

0)bO O >—

1

00 PO ir>

ra O oo PO LT) LD PO fN 'd- oo

<_) PO LO 00 o r—

(

CN ro > <u CN PO -d; 'J; LD LD LD Lrt ID

evel A o: O o o o o o o o o o o

1 M *-» o o o o o o o o o o o

3 3 o v£5 00 o fN oo o

_o o o o o o I—

1

r—

1

I—

1

I—

(

I—

1

CN

_o o o o o o o o o o o o "(5 "(5 o o o o o o o o o o o

Li- LL o o o o o o o o o o o

A-65
>

(JV

CL

njE

E

to

o o 4-1 IT) m

Eo4-»

c E

o o> —

I

O C30 T-H OO o o o

5 ^ t) t) <u cc DC a:

OOCDOO^OVD'^CSI OiX>CT>CO'X5<*>-HOO>£>

000000000

(/) w) in

o o CN 00

o

-oo o o

<<<<<<<<<

_ c

O "U TO S

-f g

S b

« E

c

V

»—It—i^OOi—lOOCTi^i—lCO<—

I

ooo'>f*r>Lf>L«'<*r».<oco'^

<—ir»-CTicooocor^OLOCTii-i OOir>':t"<t(r>cnCN<N---IOO oooooocziciooo

00000000000 o--icN<r>"stLr)vor»-ooo>o OOOOOOOOOOt-i

ni

k_>

0

c

0

"in (JV 0

Q. 0-0

0) -M

btO c

(TJ > 01 0) >< V

— 00

\ '

' \\1 I

C<1 T-(

in

0)hO I—

1

00 1—

I

01 CN CO CN lO CM OJ

k_ m r- 0 0 00 CM 0 CN LD u LO 00 T—

1

VO 00 0 I—

1

> 00 in LD VO r»- r«-

svel A 0 0 0 0 d 0 0 0 0 0 0

_i

4-* 4-* 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CN 00 0 CN 00 0

_o 0 0 0 0 0 I—

1

I—

1

I—

1

1—1 .—

1

CM

_o 0 0 0 0 0 0 0 0 0 0 0 "(5 "(5 0 0 0 0 0 0 0 0 0 0 0 L!_ 0 0 0 0 0 0 0 0 0 0 0

A-66
3

U

0)

01

ra

0)

•4-1 to

raE

E3

CO

c E

O <T> LD o oo oo ^ O O vO

T3" > £

B -J

«l OJ V

Ql Od QC

oooor^vovovot^

I—icT>oor^LnoO'*>—ICO X)LnLOLOLr)^<ncM.—

I

ooooczioooo

W W Ul M U) (O (/> o o CM CO I—I <N Lft I—

I

<<<<<<<<<

raO ra — -c > w > 4, 3-aV o

O .2 tiE

CI «->

a: ^ E

<-(Lf)r«.ir>ooLf)CDO)PO'-H<o CT)CNOOO'*CT)<r)OOOLjr>CN

r^oo<TiLf)oo<rNr»-^vc>cr)<-i t^UO'^l-'sJ-pOOOfNCN^OO 00000000000

00000000000 o^CNcO'^-LOvor^-oooio 0000000000>-l

>

oc

o

's2

V 4-1 bO c

ra ra

CNLrtCOCOCO-—I'-Hr-iO'-HCTi Or-IOOOO'3-t«-.i-HOO^r^C31 'Ovooor^'-Hooor^CTiOr1

00000000000

00000000000 OCM'd-i£>OOOCN'^-*0000 00000<— 00000000000 ii-H>—ii—ii—ICN 00000000000

A-67
3

3

4)

CL

(J 0^

nj

•4-1

njE

E

3

to

3 ^

C

o.

o

bO

4)

c E

3 3

o po O C^! VO

CM o

S 5 «J '5 -SJ J^i

a> 01 0) OH

OOPOOPOOOOJ^- OO00O00t^'S)-00'£> OCNO^vOOOOOO'^CN

ooooooooo

(0 V) 10 V> W «> U) <J o u o o CM 00 o o o o

<<<<<<<<<

4> ce o

0) *; C 1- c O <u ro £ -i s

2 i

' o

a: -o

3

c

c

0) <u

C31^-COLf>0)<OLr)CNI^Lf>0

Lft'ia-'^COOOfNCNr-HOOO ooooooooooo

ooooooooooo

oooooooooo^ bO C

nj m

</) <ubO h- I—

(

o a> ^ (U o> CD CNI CN (>- u o o CO in oo c» Ol > <u LO Ln Lf) IT) in Lft in

svel A q:: o o o o o o o o o o o

_i

*-> o o o o o o o o o o o

3 3 o CN vO CO o CN c» o

_o o o o o o <—

1

I—

(

t-^ <—

1

CN _o o o o o o o o o O o o

"to o o o o o o o o O o o

LL o o o o o o o o o o o

A-68
>

=1

V

,-ioovo^rMo oo^oo o i-too<£>^ojo odoo '55 =

m•4->

E

E

3

t/)

O O 4.J IT)

1 3

c E

01 z

O Ol IT) OO ^

'd- o o in

« c •

•w S -1 M — . 4> 4> «) Q: Q£ Q£

ooooooo(^a> OvO00LnO<-ilf)vO*£> ocTi<oif)iDa>cr>ir>o

ooooooooo

I/) U) U) mUUUUOOO W) U) U) 10 <J o o uoooo-o-o-o

-5 o o o

lf>i-l.-(CMCO»-HCVILf> oirooooo

<<<<<<<<<

O « nj :2 -f s

5 o

>

0)_ 4> w 2 E ^

I w « «

d: -S ^ E

LOCN00<r)<ncNCTi'*if>ir>co CNO»r«-OOlf>OOrHCNOO^OOJ

m^tfOfncN'—ii-Hoooo ooooooooooo

ooooooooooo 0'-icN(r>^tr)vc>N.oooio OOOOOOOOOOi-i

>

o

c

o

4> (J

bO c

(AV

bO 00 vO 00 I—

I

r-l t ra <5 oo CN CO LO o o oo T—

1

o •si- I—

1

VO o ID 00 <—

I

CO ID 00 > 4> CN CO CO LD LD LD

evel A Od O o o o o o o o o o o

_i

-*-» *-> o o o o o o o o o o o

3 3 o CN vO 00 o CN oo o

_0 o o o o o T—

1

r—

I

rH CM _o o o o o o o o o o o o

"(5 ~m o o o o o o o o o o o

LI. LL o o o o o o o o o o o

A.69
>

3

C

o

oog-oo o oodo 'to =:

>%

CDE

E

o o 4-> LO

— eq

c E

3 3

O) CT> CN 00 LO 00 r-H O ^

a> a> 0) q:: ci: ce:

ooh-0(r)'=j-fMooo CNOO'^'S-^OOOPO t^LTieofOT-icooocNOO COOOOOCOCOCN^i—lO ooooooooo

I/) I/) l/> «)

(J (J <J (J

VI U) I/) o o o CNI CO

o

-ooo

o

-oo o

<<<<<<<<<

^ .12

<u <u

OOO'^h-O'-H'-HOLOOO <^O>C0>-i00'*'*.-lt—l(>JCN aiT-i(r)00CN00'3-CM>-HOO ir)cr)OJ>-H--ioooooo ooooooooooo

ooooooooooo o--i(r\ico'>i-Lri^or^ooo>o oooooooooo^

w

>

O

c

o

!!2 'u in

pre

;DOj

0)bO c

(T! nj>

><

^ o .-I M n

(/) <ubO 1 CO ID ID ^ ni CM oo VO 00 'd- Oi 00 o

o CN m oo 1—

1

m ID r*- 00 o> 1—

1

> V <—

1

CN CN CN CO CO CO CO CO CO <

>

a: d d d d d d d d d d d

1 <-> o o o o o o o o o o o

3 3 o CM <a- ^£> 00 o CN ^ 00 o

_o o O o O o i-H I-H 1—1 J—

1

I—

1

CN

_o o o o o o o o o o o O

Fal o o o o o o o o o o o LL. d d d d d d d d d d d

A-70
3

9) a:

rau

>>

CDE

E

to 5 4) eg^

3 3

CTl ^- OO

'a- LD O

-D

<U c •

js JJ ^1 0)0^0)

a: cc q;

OOOOOO'^CM^LO CN'^OOi-HCMOJ.—(O^CT) r^CNCTiOO'^LDOPOOO "^"^ootncncNOji—lo ooooooooo

U) U) U)

t/1 (/) m

u

O O

-o -a o o OJ CO <<<<<<<<<

Od .S!! £ a: o

1)

c >

'— (O

(0 d_> U 4)

q; -a

00i£)C000'-i<O00O00'd-O ^OI'S-aiLO'-i'.DOvOh-O '^'^OOLfti—ILCOO'-HOO OfOOiCM'—I"—lOOOOO ooooooooooo

ooooooooooo

o-—i<NPO«^-Lr>voi^oocTio oooooooooo^

TO

>

Oc

o

'in "u

pre be

•«-> bO C

m nj>

><

a>hO CO o LD OO PO

nj

ns o LD V u OO OJ LO r«- Oi o CN OO '3- > 0) 1—

1

CN cn CO PO PO "aevel A ce o o o o o o o o o o o

1 -M o o o o o o o o o o o

3 3 o CN 00 o CM 00 o

_o o o o o o r—l 1—

1

1—

I

1—) t—

(

CN

_o o o o o o o o o o o o

Fal

"(5 o o o o o o o o CD o o

U- o o o o o o o o o o

A-71
3

3

u

.—

<o

4-1 CO

>>

IDE

E3

to

o

E

•=» o

^ 0> 00 O 00^ i-< ^ o to

« c • S 5 IS •5 i 9> 9i V

OH OH a:

oor^oooo^iovo OOOCNvO^CNvOTa-co OOOOvO^lDfOOCN VOOUOLOLO^COCMi—

I

ooooooooo

V) U) Vl

o o

o u o o

-o -o o o

.-i CN <<<<<<<<<

OH .!2 5 a:

nic !! c

o «* «>

— t) o

= s -

C !? E

4>

I "

o; -§ ^ E

c

fNI^OOOOOLDOOOfOOOO t*-cr)CNr«.oooo^<T>o^vocr>

oqiqi«^cnoocNCN.-;oo ooooooooooo

ooooooooooo Oi-l<NC0^ir>v0N-C0O>O OOOOOOOOOOi-i 4) >-> bO C

bO CO o ID vO CM o

(Q a> -el- 1—

1

o CN oo CO 00 CN 00 u oo I—

1

VO CJ> o CN CN CO > V cr> ID un Lf> Lr> IT) VO v£5 vo VO

svel A OH o o o o o o o o O o o

_j

!-> o o o o o o o o O o o

3 3 o CN VO 00 o CN VO oo o

_o o O o o o t—

1

T-H I—

1

1—1 1—1 CN _o o o o o o o O o o o o

"i5 H o o o o o o o o o o o

u. u. o o d o o o o o CD d d

A-72
>

3

u

c

o5

nu

_J2 '43

(0

i-> CO

>>m

E

E

3

I -o

k.u

V

hO

oID

c E

3 3

LO

i£> 00 00

CM ^ 'd- O) o

*• C

O) TM (U .t Ji ^ 0)0)0)

oooor«--OLf>L«^- i^vOOOOOOOO^-OOCTi r^«^CN'—icyiCTiCNoooo

000000000

O "o -a O LO O o00

(JO

-oo o o o o

<<<<<<<<<

o

-a

0) c 1- c

O 1> TO — -C > in > 4) (J —

<u

<u

4)>

0) E ^

c

0) O) -S ^ E

CNvor»->—lOOOLC^oocTi OOI1—IOOOOr«-rH^£5Lf50

OOt^'—iLOi—l^OO'OOO'—10 i£iCOfOCMCNt-l>-IOOOO 00000000000

00000000000 O"—ICNP0'*LniCt^00CT)O OOOOOOOOOOt-H

s) O

TO TO

!r: >

(/I a>bO 00 r- 00 0 00 I—

1

(TJ ca 0 .—

1

LO 00 •a- in LTl

<u u 1—

1

(—1 LD Oi T—

1

00 'a- LO > 0) fN cn 00 00 00 '3-

:vel A 0 0 0 0 0 0 0 0 0 0 0

_i 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CM 'd- 00 0 CM 00 0

_o 0 0 0 0 0 I—

1

t-H 1—

1

T—

I

t-H CN

_o

"(5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

U- LL 0 0 0 0 0 0 0 0 0 0 0

A-73
10

'•*-» m

Cn

>>

njE

E

3

to 2 -

c E

OO 00 OO ^ ^ O <£> O Lft CO >—

I

-a jj S 5 «

'iL Ji JT'

0) O) 4> ii:

rd-ooo';t-^-(r>4'^co

ooooooooo

U) I/) l/> 1/1 (/)!/)(/) u u u

o o o

-T3 -O -D O O

o oo CN IT) <<<<<<<<<

O) _ c

O <u nj

—' 0) o

§ S iu

OJ2 E

3

c

c ^ E

oocNr-ioo^oor-r«-CT)CN.-i >-ioooor^OLr>oo'*OLf>r-i

r>~'<d-'a-co<r>csir-i.-i.-ioo ooooooooooo

ooooooooooo o>--i<Noo'^u^vor--.ooo^o OOOOOOOOOOr-i

r-< O rH CN CO

4)bO T-H a> in OO CD IT) CO

«)

tn o 00 -a- o r-l 00 o T-l 00

(J o o o CN CO tr> vO > 0) CO LO LD in ID LT) ir>

Eivel A o: o o o o o o o o o o

1 *->

*-" o o o o o o o o o o o

3 3 o CN v£> 00 o CN OO o o o o O o T-l .—1 t-H rH CN _o o o o O o o o o o o O

15 o o o o o o o o o o o

LL. o o o o o o o o o o

A-74
oq ^ CN o

o o o o

o

'.—

0) CL

CM

>-l OO 'd- CN O

d o o o

u

01 cm

to

>»

IDE

E

3

t/)

CE

u <1> E

c E

CM O) <r>

C*7 00 oo ^ "S- 00 o r~-

<u c • O) TM 1) Jj

O) 0) O) oe: oc DC

oor>^o<r)'*<r)0^- oo'*0'-HLr)LOCNLf)r>- ^0(rN<-HOO'*LOvopo«*

ooooooooo

in in tn

(/) v> in (/> O O CM 00 o oo CM IT) <<<<<<<<<

<t: -o

O f (0

= « >- Si;-?

G 2 E V

I <u <u o: -g :5 E

OO>t-l"*vC>0000CMa)CT>O CMCM<r)0^<T)UO^fOO^O>^ OOW'^i-vOOlOOO'-I^OOCM OO'OLO'S-'a-COCMCM'-lOO ooooczioooooo

ooooooooooo O"—iCMPO^LOvOr^COCTiO OOOOOOOOOOi-i

ni

0)>

O

c

_o

'.—

1/) 0) u

Q. O

TJ

«)bO C

nj nj> 0)><

(/) <ubO 00 oo <y> Ol 1—

1

o o LO h-

«] VD LO 00 CvJ CM 00 00 "S- 0) <j N- CM >£> o CTi LO r~- o 1—

1

> <i> PO LO LO 1

5vel A o: o o o o o o o o o o o

_i o o o o o o o o o o o

3 3 o CM •cl- vO 00 o CM OO o

_o o o o o o (—

1

1—

1

1—

I

1—

1

CM _o o o o o o o o o o o O

"(5 15 o o o o o o o o o o o

u. o o o o o o o o o o o

A-75
3

u

3

4>

n

u

at

m*-» to

>.EE

3

CO 1 ^

c E

3 3

CTi 0> O <0 CO

OO <£> o o in

• — > k_ i; _'

4> 0) 0) Q£. C£.

OOfOOt^-OOOOLDO 000^-CT>OMCM'*OrM OOOLOCOfMOOOO'-H vOLftLOLOin'd-cO'—irH ooooooooo

U1 (/) u> (/)(/)(/)(/) o o CN CO o o

CM IT) <<<<<<<<<

^ .S2

_ c

O « nj

U) > 4> o — So> . .« Si ^

« E

3

c

q: -S ^ E

opooooooicooooo^o OvOCN'OlOCOOOLriOOOCN

r^LO'd-'S-forMi-ii-iooo ooooooooooo

ooooooooooo 0<-HCNfO«*Lf)^Or--OOC3>0 OOOOOOOOOOt-i

>

o

c

o

'«/)

k! «« --g

bO c

ni ni

(/> 0)hO CO o O) CO CO in o I—

1

nj

ra O) o a> O) in o CO

0) <j CN o CO oo o CM CO in > 0) CO "51; in in in in in

ivel A o o d o o o o d d d d

_i

*-> M o o o o o o o o o o o

3 3 o CM <lf 00 o CM oo o

_o o o o o o I—

1

I—

1

I—

I

.—

1

1—1 CM _o o o o o o o o o o o O

"(5 "ra o o o o o o o o o o o

Li- u. o o o o o o o o o o o

A-76
1—(00^0'<*CNO oOj-oo o

<—IOOi£>'^CNO odoo 'v> —

"(J fo

CL

CO

E

E

3

<J o ^ LD

TOEo

-Q ^

E

c E

3 3

OH 2

w O <u OO O <u O O 3 LD t-H CT

<1> <U (U q; q:

bO OOPOOOOOiCNCO

OuniDLOLO'^-OOfN"—

I

ooooooooo

(/) 1/) U)

i/> w 10 o u o o o CM OO o o o o CM un <<<<<<<<<

a: o

-o

-C >

> ^

E ^

c

-g :5 E

r^-—icNor^oooiDO'^CN OlLOOOOOOLDOOLDvOr^'^ OOOOOOO^nOOOLDOOCNO P--LDLr>'*0OCO<N'—lOOO ooooooooooo

ooooooooooo

OOOOOOOOOOl—

i

TO

>

O

cO

u

<ua. o

-o

i)bO c

(D >

><

(/) u

W) LO 'J- I—

1

CN <NI CO IT)

(TJ o I—

1

00 CM OO CN CN >£) OO Ol '3- r-- o CN "a- 00 > <u OO '3- ID vO vC <

"55>

V

o o o o o o o o o o o

1

•«-> o o o o o o o o o o o

3 3 o CM 00 o CN '3- vO 00 o

_o o o o o o 1—

1

.—1 I—

1

i-H I—

1

CN _o o o o o o o o o o o o

Fal "to o o o o o o o o o o o

o o o o o o o o o o o

A-77
CMO

<>

to

>>E

E

3

CO EC?

c E

0^ 2

o oo O CO >—

I

Oi O LO

LO >—

I

"3

<U c -

• 1) TM O)

• — ^ Iw _|

O) lU <u 0:: ct:

OO^-OI^CMOOOOOO

criooLO'S-i—ioor^oo<—

I

ooooooooo

(/) 01 (/></) <-> (O Ul (/)

O -o -u

"° O LO

Lf) ^ r-l o o CN CO o o

CM LO <<<<<<<<<

o

c >- c

O nj ^ >

— 5 ^

q: -d

> . a> Si I E

c

en

ocNi^cooir»-i—loo's-"—ir«- OON-CT)LOOi'S-cr)'*OLOO

vO';ffO<r)CMfM>-H>—l<-HOO ooooooooooo

ooooooooooo Or-HCMfO^lOvO^-OOOlO oooooooooo^ hO c

nj TO

(/) <uhO o CM 00 00 o rH O) CO oo CN

nj CN CO LO CN o LO

<u 00 r—

1

CN 00 Ol > <u CN CO LO LO LO LO LO

svel A o o o o o d> o C5 o CD

1 o o o o o o o o o o o

3 3 o CN 00 o CM oo o

_o o o o o o r—

1

1—4 <—

1

I—

1

I—

1

CM _o o o o o o o o o o o o o o o o o o o o cp o o

U- o o o o o o d> d> o o o

A-78
1—lOOvD'^-CNO OO^OO .—lOOvO'^-CMO oooo

|w —

tom

E

E

3

to

o o

(0E

o

-M3

"lor

O Ol ID o 00 eo O --H O vo

LO .—I

*» c •

• « 5 «

i: j; _|

a> a> 0) q: o: 01

1 3

(J

rage: isior o oCN o o o oo00

LOa>

I—

I

o CN

(LI q: o LO

LD

1 Lf) 00 CN

where

i-H 1

Ave Prec If) o

If) O o o oCOo o o

T—

1

o uojs;: levani COd

> V

Q. o

_j

inu

0)

4-> r— vt U) in C >

mei U) lA (/> (/) o o O O o V

U) o U u u o O O T3 -Q u O O O o -a -a "O O

l-> 3 um o

Oo

-o -D -o -o o o o

"u

in o LO o o o o o O *j qI

1

c 4-) in i-H rH CM CO I-H lf> I—

I

docs c

OJ

(J

ni < < < < < < < < < ai 4-> E

X

LJJ

CT>O1lf)lf)CT>00vOvOCT>vOlf) OLf)<*co^co'*ooo>Lr>oo vOOIi-nr^-CNVOOLOt-ivOO vOTa-<^COCOCNCM<-H<-IOO ddddddddddd

ooooooooooo Oi-icNco'*ir>>£)r»-oo<T)0 0000000000.-H bO C

V)

0)bO o T—l CO 00 in fO in 00 vo in

ni^ (0 I—

1

CM in o in in in

0) o o 00 >—

1

vo r>«- O) o r-H > 1) CO CO m in in in vO vO

evel A o o o o o o o o o O o

_i o o o o o o o o o o o

3 3 o CN <a- vo oo o CN vO 00 o

_0 o o o o o I-H 1—

1

I-H _o o o o o o o o o o o o 15 o o o o o o o o o o o

U- o o o o o o o o o o o

A-79
u

V

.!2

*+-» <o to

>.

njE

E

3

c E

O <D OO O 00 O) o 00 O O LD

Lf> I—

I

-o 4J

4) TM (U i: J? Jj

a> 0) 0) ct: 1:1;

OOh-OCOvDOOCMO oo<^oo^Oi-tOLr>oooo ON-P0.-IOi00O^00^

000000000

W) W) (/> 000 O

""^ O IT)

Lft .—I I—

I

O O CM CO o o

<<<<<<<<<

q; .2 J,

I- 0

«j o

c c

u) > <u

-QE

3 u

a: -g

'^CNOOOCOO^N-OO^iOO cs'*<-H<NiOfOi—i>-ocy)i£i Lf)00L000Ol000CN00'=d-O r^-LD'^cococMr-i.-iooo 000000C30000

00000000000 O'-|CNC0'!l;iD>£>P».00O1O OOOOC)00000<-H

nj

4)>

0

c

0

w

'u

(U u

Q. 0-0

a> '4-' bO c

ni > 01 0) >< <u

O »-l M CO

0)bO LD in 0 0 <£> 0 t—

1

If) I—

1

nj

k_ ni r—

1

cn CN 00 tr> cs .—1 CN

<u 0 CO CN If) O) 0 > a> CO co IT) If) Lf) If) <£>

ivel A 0 0 0 0 0 0 0 0 0 0 0

—1 M 0 0 0 0 0 0 0 0 0 0 0

3 3 0 CM 00 0 rsi 00 0

_o 0 0 0 0 0 1—1 I—

1

I—

I

1—1 I—

1

CN _o 0 0 0 0 0 0 0 0 0 0 0

15 "(5 0 0 0 0 0 0 0 0 0 0 0

LL, u. 0 d> 0 0 0 0 0 0 0 0

A-80
>>nE

E

3

<U 4) JQ ^

c £

3 3

01 z

CO CM

CM f>- CM O 00 O LO

-a iJ > ^

01 0) <U q: o:: ci:

ooooor^oovDoi'Sooooooooo Lf)'*Lr)ii5iX5'!}-(r>CMr-l

V) U) (0 (/> o o CM CO

o

TJo o o

CM ir> <<<<<<<<<

c 2^ c

O I* <TJ ^ >

- 5 ^O)

o

J? E

q: -S ^ £

OCMOOCOt-ivO^OOOOO CM^vOf^OLOLOOOOO vOLO'd-CMCMOOOOOO t-HOOOOOOOOOO ooooooooooo

ooooooooooo OrHCMco'^-LOixsr^ooCTio OOOOOOOOOO-—

I

<0 u

bO C

4) S

< ^

1/) 0)W) 1—

(

0-)

nj TO CO 1—

(

oo CM CM CO LO O CM CO CO CO 'i- LD LD LO > 4) O o o o o o o o o o o

;vel A o o o o o o o o o o o

1

-*-» o o o o o o o o o o o

3 3 o CM \o oo o CM 00 o

_o o o o o o i-H I-H I-H t—

)

rH CM _o o o o o o o o o o o o

"to "to o o o o o o o o o o o

u_ LL. o o o o o o o o o o o

1 A-81
>

niu

3

0)

.—"•<->

to

>.

n>E

E

3

to

<J o

(TI E

o

3

ns

c

o

Q-

(H

I CNCOQ

<

E^y

c E

3 3

oo

PO h- O vO

CO LO T-H

>

.22 >

i: -22

0) 4> 0) Ql OL OL

OOOOOOOOOO'^d- OOv£5CNOO.-ifOPOCJi

^.-H.-lr-lr-<r-IOOO ooooooooo

(/> (0 (/) l/> o

Lft I—I i-H o o

o

-oo o o

CM LO <<<<<<<<<

5 q:

c

0) <n clis

So

Q. u)

' o

-o

« E

on

CT)CM'a-OLft'>4-«*CMrM'—i<o CTiis-OvOOOOO'-H'—ivOOCTi i^LOfOi—lOOOor^vOfOCM^ fNj^^^OOOOOOO ooooooooooo

ooooooooooo o^cM<r)'>*if)'£>i^oocy>o oooooooooo<--i

4) (J

hO C

> ^2

O .H N W

VI

0)bO o 00 CO CO CO CM I—

1

CO

«)

nj o 1—

I

t-- o VO 00 o

<u o LD 00 o CM 'a- in VO 00 00 > 0) o o

ivel A q: o o o o o o o o o o o

1 o o o o o o o o o o o

3 3 o CM '3- vO oo o CM VO 00 o

_o o o o o o t—

1

r-l .—1 t—

(

CM _o o o o o o o o o o o o

H o o o o o o o o o o o

u. o o o o o o o o o o o

A-82
3

3

01 01

Of

E

E

3

C

o

taO

A-) rau

I CMX

IEcy

Ti- OO <£> OO

<J) O OO

.-I CN

>

=

01 5 <u

• — ^ k_

-<-> — —.' V V V

Ql on on

OO^-OCOOLC'^-'JfOCNCOOOCNOvDCOi-l

1—(i—l<—l>—(r—li—(OOO ooooooooo

tn i/> </> ut in in u)

T> "O "O "O O O O >—I .—I CM CO o o

CN ID <<<<<<<<<

q; .2 J, « o: o

C C

O 4* nj :l 1 s

= s >-

"

1

c

Q_ in

a: -g :5

';fO)'-lir»vO'*<-HOOt>-CMeN oocN<or~-Lr)t^CT>oo»-ioo <jiovo'*or»-<0'a-'—lOO CNCN^<—li—lOOOOOO ooooooooooo

ooooooooooo Ot-lCNP0'*Lf)<£)r^00<T>O OOOOOOOOOOi-i

4> o

hO C

ni ns

(/> V

bO CO CT> CO vO I—

1

o r—l IT) nj

ni CM ID CO lO h- CO oo LD Lr> o

v (J o ID o CO in oo oo 1—

1

CO > V I—

1

»—

I

CM CM CM CM CN CM CM CO CO

evel A o o o o o o o o o o o

-J

4-1 -M o o o o o o o o o o o

3 3 o CM 00 o CM vO oo o

_0 o o o o o I—

1

>—

1

I—

1

1—

1

I—

1

CM o o o o o o o o o o o

Fal 15 o o o o o o o o o o o

LL o o o o o o o o o o o

A-83
Z3

a.

4) a:

3

TOU

V

a:

u

V

y o '-4-> Lfi (/) O TO .SJ O

E

* k-

<u I—

1

!-> o 3

3

TO H

QQ

tics ory

ove

W)

'*-> (U -4-> TO

j-> M c

TO <L) U

1 E

cu

TO LU o

E -a E o 3 (0

ber

uerie mber

E a 3

C

-b 4-> 3 <^- <i> c O "to > TO <u

un

unn Tol etri

ele>

1 0) a: a:

oor>-ooooinin^-

ooooooooo 'S-CNCNLDOOOCN'-IO

I/) u) in y

V> U) (/) </> o "o -a o ir>

IT) t—( .—

I

o o

<<<<<<<<<

>_ o

4J q: o

<+- ^

TOC £ C O flJ TO

2 E

bO

a:

eooooooooooo OJOOOOOOOOOO ^oooooooooo OOOOOOOOOOO OOOOOOOOOOO

OOOOOOOOOOO 0'-HCNcO'^Lf)^or~-oocriO 0000000000>--i

TO

>

o

c

o

'in 'o in

<u o

Q. o

T3

+J hO c

TO TO> 0) 0) ><

o ^ cs n

I/) bO o r»- o I—

1

o CM

TO

TO oo in oo r>- oo 00 U o o o o o o o o o o o > 1> o o o o o o o o o o o

ivel A o o o o o o o o o o o

-J

+-> o o o o o o o o o o o

3

3' o CM \o 00 o lO 00 o

_o o o o o o I—

1

1—

1

I—

1

I—

1

CM _o o o o o o o o o o o o

"to "to o o o o o o o o o o o

U- u_ o o o o o o o o o o o

A-84
3

0)

nio

niu

01 ct:

+-•

>,E

E

to

O O

niEo

00

c E 3 3

o •«^- OJ O VO O O .-H CN in

i: ^ Jl'

a> a> o) o: Qi:

ooooooooo ^CNi-Hi—It—lOOOO

(/) w u) u> <-> <J u o o CM CO o oo CN IT) <<<<<<<<<

kOCNLOLOt-<00000 cy>oo^o<-<r-i'— OOCNOOOOOOOOO looooo

ooooooooooo

ooooooooooo O'-HCNPO'^LOiON-OOCTvO oooooooooo.--i

ra

(1) >

O

co

'u) 'o

pre DO T3

<u J

bO C

(Q to>

><

<1>

V

hO

ro

nj o OO r- 00 CN CN oo oo oo O oo CO CO cn en > lU o o o o o o o o o o o

Level A o o o o o o o o o o o

!-> o o o o o o o o o o o

3 3 o CN >£> oo o rsi VO 00 o o O o O o I—

1

1—

(

I—

1

1—

1

1—1 _o o O o O o o o o o o o

"(5 "(5 o o o O o o o o o o o

U- LL o o o O o o o o o o o

A-85
3

3

o

0)

I 3

o

niuV

>^EE

3

o

rt 3

c E 3 3

o cr> N- 00 \oo o <r) LO .—I

O) p > m

0> 01 O) q: q;

oocoooooj^oco ocncoo^lolot—ir>-i—

I

cnor-HoovoinoovD

ooooooooo

</)(/>(/)(/)

(J <J <J u o o CM er> o o

<<<<<<<<<

4- ^

4> *i c c

o <u n

S -i g

"S o

> .

« E

3

q: -S ^ E

Lr>CT)OOLf)r«-oo<—(incxsoo vDOO<*CNOr*-vOO^OOOO o>i—iLnoooLocor-looo MOJt-Ht-iOOOOOOO ooooooooooo

ooooooooooo OT-HCMPO'^LftvDto-OOOiO ooooooooooo

<U (J

hO C ^2

CM w O

(ft a>hO .—

1

CO 00 00 LA CN CO

<TJ tn h- .—

1

r-H t»-

0> <j CM CN o 00 O) »-H CM CO > 0) I—

1

I—

1

CN CN CN CN CN CT) CO CO

svel A q: o o O o c> C3 o o O d> o

—

1

*j o o O o o O o o o o o

3 3 o VO 00 o CM 00 o

_o o o o o o I—

1

«-) I—

1

CN _o o o o o o o o o o o o

"S H o o o o o o o cp o o o

o o o o o o o d CD o o

A-86
APPENDIX B

This appendix contains charts created from the supplemental forms filled out by each group about their sys- tem. These charts are meant to supplement the papers and contain a standarded and formatted description of

system features and timing aspects.

B-1
o 1)

T3^ a c

'Su

•oc

OX) cC3oucoZ c

'S

<u -o

.5c

•c3ucoZ c

'S

(U T3

.C

W)G

•c3

TD

<UCo

00c

<u TD

.C

OX) •c3

•o

<uco

OX) c

•Hu

T3

-S

00cc3

•o

<UBO

00c

•a

•T3 .C00c

•E3

T3

0)co

2:

00c

-o.c

00c

•c3a

00cR(U T>

.S

00c

•c3

tUco 8i o ce3

(25 o cc

•Ho o 6

•T3c3E

t;oQ oI oS3 co

00

00o

"o

Bro IS e© eg3Ozu

4>ao Hcc Q3s

.2*

llO X

B-2
I co 3 ^

^2 •aI8 rJ o

-a

J T3

T3

i_r ^ ^ of)

•§>•§ § g i a ? * u See* § I a « V "a B

r- r- 3

•aiu3 I

in38oI

•a

•a

.S32

i2 •aI3

cr cs

00

.332C«^ ^ i ^1 -CCSi2 ^ u o ^ O "O rWOOc O ^ 1^ c§

60Oc3oc>ao

.3 S fli o

5.oc ^ -5 13 C

O+d

II So

B-3
00

D

O

D

U

<

00

v5

0)

O oZ

4>

O

Z

o

c

o

oZ

o

1>

C3

O

c

2

a.

z

u

z

o00 oZ

oZ

oZ

oZ

2 o

oZoZ

a. £

Z O

oZ

oZ

oZoZoZoZ

oZ

8

c

o

w

c

u oZ

oZ

oZ

c

o

OZ

oZ

oZ

oZ

a,

oZ

oZ

Q

S

C

oZ

oZ

Z

oZoZoZoZ

o

oZ

<

Z

>- oa

o

o

E

3

z

n

"3

I

o4>

e

o

e

EE

55

S

•no

C/2

S .S 3 ^

O §

Q oi

c

cay

o

e-o

'5

on e

e

n3

Oil E

oZ

oZ

w

41

1.U

oU

X *= U £

e

©I

o

a

1/3

oZ

c

"3w

oZ

h

I

oZ

B

0)OH

E

J3

O

Z

H

s

s

es

01 3

3

sO

'3

ea

3

£ 2

O S>

B-4
e3a0

EdUH

Vj<aHOQOzuaz0Z©3Hz0 5z c

•HV

.5I

a.oZ

•aoc

.22

4>B&u3

00c8

•a

.S

00O

*«co

t3

Z3V3Cu

ID « o c .S

o.

•2ot

CMU 1 = F 3 EOVZ

E.3Ou 8 8

00cS

t: i2 S - 5 Q § r £ a

C .: 00cc

.0«o 2

•c

v>Co

55I EV • J=

•«

V 00 " c fI

"I2 »

to

W o « ^ § E §

«:§ = ^

•o .a

00 oo

55 55

"8ou

.c

[2u

=3cn

.Sc

a.u

4>I-9« Oc

00

Hi

•a3

•o

.5o

V)U c

4) Io ^ 5

<M

O

O

w ^ u = 2

.SPU 2 (is ^

V. 2 c

x:a.

•c33

H.B s s " .s 0 o

4> .2 1 i£ 00 > a O

in vo

B-5
oIT)

60 ^

•c '-'

St- o

C/2w

O o o o oZ oZ

00

rt 60 oZ oZ oZ oZ 5 S o ^ i ^

00 2 1/^ ts oco

QQ oZ oZ 1oZ

«0 Q oZ oZ oZ oZ oZ oZ oZ oZ oZ oZ oZ oZ po S o^ ^ ^ co

-o S3 d o a Oh ^ *-l

w ^ (U d| 8 Z a a oZ oZ oZ

•a3oZ

eoc

S3 oz

•ioo

•cO

00 c

-5oO

"oo o

OSo

'.Qa

CO1oU

•3 e l|U £

II e

"SwoZ u1

f2

•o5

739o 5

es Q

•3

eao3O CM

B-6
1

•a

4> o§

3^ ooupo aoai

a.o

IS 1I c

.3s

C/3 ioS

C4i

Oro ^ 8uso3u

CMe

a*a

•23ao33IO

C/3

•n

CQc

.2«§c3

"3, 3

T3 ^ ^ •§

t:O

Phco

0a

a3Oc3

•o

(1> a8

4>c

Ui Q

V3

.S

0)D,

-a3C0

<L> 3

or

73Io I

8^

as

a.

a

C/3

a,

"5.

caO3

TJ

lUN18

ta

caauSo

<u

a + S rii + a °. c

T3 *3 <j +o c

II 3 £ S"

=3

00

a

"2

'5cI 8C— 2

eao

'5*iec 3 ^

"Iu

ca or § .3

t3•a §

ca

g

C T3

•a

T3 .a + 3 ^a ^ Cm > c c c

.3 § -a

4>3s

•a c

caX

.38oc13

B-7

4>

QO

•Of

JS

«2

4» 3ssSooa

13

3t

IS

"3s3

"3B99

€3

•a; n

ed) 1JT3

is

*-«• 0 SB

an1

a. I^ a ^ if^ as, c a a » ® "H o s 1 s so

9SS3as9

or

<S3t a3

a.i

T3 ^ IB

S3 =; o9

3.

cos

J3a 2 S a3 WD

ataaiss3ad

•Hi

3»

•3a3 - a

SO) aO sa3

T333

4»

"3 * i

fa. .3 aJ

BO . w OS —

3 K« > ® &

ja- 3

Xi » 3Sto

T333i3

"ai

4£ if 1 S ^ o a« is

B-9
Q

Q

00 ^

00 o

C/3 w

1/1

QQ

PQ

4) ou o CO

On

O00 00 ^

0^OQ

V3

ag. VO « 00

CM as00 ^ Ot-»

13

."75 3

PQ

-ao

.S

•T33

pa3

t2a

•8ca

•c 2

COSo3<

S23O3O

i5 Z *£

pa

UiO

132 H

•o3

PQ

13 3

•8socoI a

PL,

CO 3o

133

i5

B-10
tH tSC*^'* U1 VQ r- 00, On

B-11
B-12
2o

COi09

CO

(A

PQOO

VO

T3a23O5M

60 .3n

.Sc

T3O

a>G

P3e

•-J 1/3c

ca I—>

•=

O

.5

iiS ^ O Oh cS 13 o 5 ^ Oh Sh8 2 S

d: 8 C«2

S2SX

(L> -o.3ao2

1S

<uC

.3e

o. . s & C W) o 13 C 3a _ 8 2 S

B-13
1^S -a« 3 w

Oz no

{0 5 H P

C/3 So es S

.5 III o in

00 0)

P3

pa' 60.2 S (5 I ^ <i>

Bo S o o ( ) U

CO (/3

•H

(U c

Oi S^ W5 U ^ O e 2 ? O X! «

PQo

.§H

T33

PQ3d ^

(2 5i

O

00O

13

H3

CQ32 §

"8Ocac I5Z b

PQ

•ara3

pas

"8c

•n I

12§X3

B-14
1ouHII c

(J

OQOzopupoe

•9aAaOcs

•f

<L> c

s:

.S

a

•aic2

X) ^ Q

«i

oj IXJ C ^ i

.S e

•5 -3 Uc I5a

B-15
fi
a o

OBQ

OBHOBs

OB5

OB

OBmO

OBmgoQ

i:u

OBou 1

"SC 1^

St. S

.ft? oSoB

••iM» Mi

'St)

TO" o

OS)

"5"

0« IB

J* o

as TB m Si

is asi m

OB

iSt 2OS ^< n « 22

B-17
CO

V3

1/3

COaawou

(15 oQ

[2

CO Z

4>

O

COa 1c8Jou

C

.2C

a>

rs

f2

133

2i Iu2I3CQ

03

UiO ca

•c S

a>

ffl) oH

.•73 3

CQ3a1 §

i2K

j2SI

"8c

.2

o,

•c i c

.2

.y

'•Bu1I

so

B-18
po uI io

CO

091Ii

09

09o

2;

09oua

u3

.S8

60E

<1>

60

.st8ogI8ScBO 89

(TI

"O3 M e8 S c 2 B § §0* O 4) H

Si I 3 ^

1| -I 3 ^ S

'3

•aa 2 i o <tt

Siao ^O2 8

(i> j> 3 gjj u

on 8 oo

a.

3>

.2

<1> o -a o o

CO

pa«co 3O

J)I Q o c

'+3

t:

.Si

00

.Soa

"2i4-*

B-19
4= a 6 3

S

« 2

C/3 Q C/3

X

o

^ S - o

00 tS "O o

o2

IS 3

o

o

8 ^ o o

s

2

0)§

C/3

COa

o

4)

O

<L>

C

6

(4-1 O

c

.2c

3

I

I

3

O

at

CQ

O

T3

03

6

ca

•cuQ

i

I

eg

CO Q

«

n

o

^2

3

n

3

a.S

S

CL,

COs

o

3

<

"8

c

.2

•c

S S

*: 73

^W8-20
4> <U

13 q2 o

9

z

o

i

i

.si 1

1

EdO

09

zM

g

z

o

z

o

u

o ^

Z £2j 00 o\

B-21
oZ

c

o

u

o

U

§

Z

u oz

C/3

9^

1

O

73C

c

O

c

.2c n

3a

t2

•a I

3

O

O

55

3a

I

c

.2

a.

•c

o

-a

.§H

.-a3

3

3 S

^2 5.

"8

c

.2c

Ji-22
po

COiOu

CO

COIc«iOo

CAUo

CAouQ II

.sa

*«Io92 5

S28

J3

60C

.S

523O

Xi

•a8

•s

t:

.s§c S r2 ^ ^ ^ t-H «S C<1 o

<^ ? S -1 t 1

•O f<l vO <S

O i 1 c g 5 8o

12 « s t: a 2 S>

S2§

Xi

.sS cI a

-a^ ^ 3 « *^ !5 .S c e 5 T3§ 8 a "o 8

.S. y 3 00

.2 -a ^ 5 Pi !2 -52 - a * -o

2i I « §

!0 o I §

II

•a .s s e « a 2P i2 c > W IS § 1

4> T3 * S i 2

ifO <u

•2 -o •^^ S 13 3 -a a 4> e s

IT S

B-23
oOU1

Oil S

01oa

C3

Mlo

KmO

asougOB 9

•4M> o1

<»o

IS

IBi.

nB

"5) 1Z

IS!

O o « s B eOXo

c; -2 ^S ai E

"3 S

IS a

am»o

SSI

JE

So 9

_2

0»B

JS

0.==© B —

fiS E o ® K u

^^^^

IBf 02

B-24
•ao

a

J5

s

o

Or

Oft is

!9 64S»

ill

8b,

IB

3>

3

at S£

4Si> or

0$

K! a

da

S- -a

TO

CC

3

B-25
c

Si

4> 61

CO

00

OSoo

P£5 oQ §

es

•3

ea

.Sscuc

J5S

I* 8co cEa<u PC 3OSo3a6 o

•3

"a

mwuO

-a

^2 c .S

\0

HaO Io3

X)ucS

>^

"S-*-» <u "3, S = 8

t;

C ca S o ^ •si i

^1 o S S ^ 8 3 s a8 S S S 5 o

B-26
»-i <N <r)

B-27
C/3 B

ona

•c3ao

on o a

<i3 S8 -2

•3I

.3 I

55 S

•a

a.ou e8| •R 5 BU S c O "O Q .Sc S °cu

T3 15 •Sc

<u •o 5 s I t C <1> c

<ac o I ^

.3 .3 S S o c

<u 2 ja -so

COuuO2

;3c

.2ca(i> 05

•a3s3 8

12OI ao

IS o o

en Io aa

05 I1S

>>

<i> = .2Jo

«*2 ^

•3 o is •a ^

IPSao

•i3-5 3 - « a 8S ^ a B B § 1 ill

W'

B-28
0^

J3

"5-

&

(A I^

i 8

J5 >^

-53)

5 ^

O

C "O

^ M M S eo CQ

tH o

u

•oo1

c

•rt 0\

o\ 00 00

a.

?Jc

o

Vc

o

4) 5 00 O

8 c ^ til

ecu

.B h O

1

c

§

8 a

'H P

3 3

O O o o

o

c3

o

a

^-v Id c V

83 "-I S £j

i2 3

5 o

O J3 •= ^ <t CM

^-1 O) f<^

o ^ ^

S5 £L

B-29
U

w

2

u

05

u

i

(5

1 ^

ecu

1

•3

ea

>.'c3 1 i

c

u

•oC u

.S .5 s a aa

o £±

5 i — 0« a -S I s e

o ^ u

1/2 0.<<

00^

I. 2

paS

O

i2

a

3

I J

I

a,3

O

3

I

c

5

.sr

c

.2c

u

Pi

XL3

O

3

e

(2

i23

o

S

o

uS

.s

3

u

<1> w

.s

•a3

eg 1

i

PQS

O*-» (/2 J!

C

.2aa

e5

•s i

.S 5

3 5

I

a

.2Jo

^1

^ .S 5 J35 S a a o

*5 *5 TM

. 8

a O

B-30
09ggoCO

e6oQ

U

(U

.S I

COH

COI ICI u§

•au

t;5 u

§8Ico

CO

COi

COQ

toOIo c

60 5

CO

•cIfI

"2c

ca

*•* II

V5

4> Io

60c

606d o3

.5i

en

4)o

.a

•a

CQ8

B-31
a

a..

J5

0jl as en ^ p%

31D

O

3

<

Q 2^

2:

1

SSi =^

"3

e

o

e

.B.S

e s

"3

as

M

3

^ m

; ^

O

GRi

SL.

•s.

an <«M*

»a2
lb

s3

<» M

a

lb

o

O

i:

u

o

oBan

•5P

4»

73) cx

IS ^

I %

CO)

O

Ct. >>

S 2 «» 9

O 3

"fi «"

oS **m s i

S i

42' 3S is 3

MY ^
cOc

C/3 D.

<i3

61) •S 1^C X)

¥ &• o Ba3

03 8 ^ oI 66

<L> O"

li

<L> a, o 6 ;S

&4

U o

•s

J3

4)

M S E g §

Si o c3s

t: a

.sso 2bu

'B. e2 o3&a© 

uo oUo1

<L>

C

60

.3

12wo

-4-* IucBz

8.2

&4 15

(t- 3

</3

DQ

ll

^1o ^^ c "3 2 .5

ii

?6

(i> J3

B-34
sIIII i

CO o

Siua9o

.3

4-* •aI

.Sao

43cus

a*

eo

.s

00

.SI§ .

13

.S 1 ^io

•aI1

•5c

.a I ^ C

i2oI§d

.0

•cu

•a

13a8a

.2

•3s

inu3eQ8c

fc3Boo

IHBO1

.3

.3

60iaiIai8§i

<U

*-» eUBUtDOc3Os

•o

<^ %

B-35
S V « A

A i Z V V (i 1 b on9

09

fiQ iI zogi2o e Z -a e

60 2 e2 = o

•n« -

.s |-S F ? O i -S 15

O

u o2 =r

vT &• ^

3S £ t>

eo ^ so I o * 6 „ "S t« 1 g I s -s I i S > s iJ k 2 o

(rt -a i s ^ E 8 I 1

8.a

a.8 2

I- c

.0a oz 1i 3JHk

B.

II

Iti 1 EI B3oZ

111

uE o o I

§1 i i

S..S 2 M OD Cd iS 5 Q

§1O§1

-I I I

16

B°36
a

g

.2 -a 2 ^

•S ° ^ i 5

•aa

u i

''3 I'i ^ i

S.I g <s 158io

.S >^ ^ 1

.a ° ^ <u

ea o b

«« 8

[2 « a §5c

caa

ta8a

<ua

ea .538o

(U II

tJIu

.3

ea33 O

60 3 3s «

.£3 I

ii ea

9- o ?S

.t! <l> "O ^ ca ^o ^« Ig 2 § oC 3 Ou 2 •3 ^ .a 0 y

•c 2 i2 « « § 1 I 8 U ea

.S 13 i ^ i2 ^ > w 8 H S

Si

ta .a 60

•s « • s „ 2 2 « 3

C8

O

<U 60 S .s c8

_e eag

G

y

e§n 2 y «>

B o xt o -O

ea c ii 60 -a K .S -a§

13 ^ -o 2 2 S O O -5

ih ,1-1

n o tfl -a C g >>

60

'5 • S § g ^ S

g^.ss s I ^ 5 3

•5

H ^ 3 § ^ 2- K § S «

Sa ° g « g *

<u3o

IhO>

ea

•a3

cr

8-37
Bo

oZ

-2 ' a o >i o

G <^ & g o g ^

ca I g

.SP

1 e

<2

.a u o, g C o

T3

U o

.S ^

g -S ^ -o y 2^ .S .s

•aI

alsf I

il

6 s

q> .2

U

i2

'•3 . n

> 5

O .S

w

f2

i

aao

s

^1

1 a

1

B '5, -as

.3u

a

C/3

c

ys

c

u

T3c

3

OZ

2

s

n. S* 2

a ;3 Q

a ^

I ^ m O

c

.2

16

B-38
uonIOizopzou

QWoQ

J5u

13 ^ 6 §

'5.

^2

•3

eao«s

o. S 3,

H

la I o .2 i 2 3

135

C/i

g

W 1

B-39
c/5

(/3 w

on

PSouoQ o

5,« B

.a (u c: 'S

2;

a

.3So8 ^

.s 3c1eo bw

'0. oH ca <u o^ .s

a

U c c ifa < I o o2 o oO

H3

CQ«

<1>

-a

.-73 3oo

2^ I ^ I §

II

.s

a «

i2o

)-> oI1

f2

T3 o3O

a> oo

-ao1

IH2 a

IH

U If

<U <L>

B-40
oI

rSI"3g1 fc> •3 <u 1 §-

•a -1

.ac ^p

li0 o

in «

^"

cr-S 8 3 u 9

IT)

C ^ r-l

Is

ll

li1 a

I?

a23

(UO

J3o

.S

(A

1;;

.s

<L> oi«

«-» Iao

•s3s

<a •C

(U3a 6 C o u ^ o A ^

^; .a A

12o

>^A

T3V1

O. c

'a'

4>

be1a

00c

t; f a -Si 2 « a

t5

A

u <u o« V

<u ^ I p

T3

t> Z86 I aoI c§ %coo

4>

4)u

(no B

_oioop3o5oB

O.

<uBO

•8s

to s ? -c

(U 3 C aSJ ^ o

J3

00co

:i

(5) 2;BB

<n

O

H < B

OB§

B* .2a

•c

133

•c

<L> U

'ao

.3

<ABO

8,i3

tiai

00

T31 I31

•3

bi 1ooB

a,3

•a>3

153

O"

•aB

00oa

B-41
O oo< 2 coutc 3

crO

.3a co

4^o

•c3a

"S

t>5I

toJ 5 bw

'S. e2

H

•3 5 eo

•roU1

.5

CQ

;2

t2

731 3O

CO

J3 oo1I S

Ifg31i

B-42
3

T3O

s

u

I

S .2 >.

r-l +r» rt'i

i

H

z

o

i

t oM

s

g

H

99z

o

u

I

DC

5 o

o 2^ 5 £

u a. a

3.

«j o

II

O r-.

B-43
I

B-44
I

43

gaaBI .S

43

us9OU

IEIoun oa2

>>u

43i

X3coaIS

iS

-S, I

438§3a

.3

i2 1a

.a8a

<u I 2u8§

.3N

4-* 3%oao , 5 o

^>.2

^1c 9^

5,o t>

4> CT* j3 g

B-45
a;

C/3

1/3 O

C/3

00 oZ

(U •c

(U as o o

"a! bu

'5.

e2

aH

•3

SQ«s

Si < u3I 1 eoeou cm

.S

[2 8

03 o § o3o

C/5

43

Oo

J3

"I

(l> M) s ^

<u <L> U (U aii

B-46

§ uoaIuQ ca3

Pi co opup

C/3 Zou

•aOZ ua e6

T3C

iS e

.2 1

"3 3 *

iS If

'o IaSou

4>s<

•ac3

Ohe 3

.S8 ca3oa3 O cu 2

60 4)

>; <u < Oh

uS

•s

X)a e«

in«C

"23s

60

'S

Pi

C8soI §ca3o«8

T3Oa

4=

-83

B-48
oo

:z;

ucoo

•oc8 § ^ 2 o

t> > o s 6 coo3

cru < TD .5 > <Z o S^ 9 •B O

•2 I c3o

i2csa3 O

00

op

05

_o

*^c3a

T3O

B-49
1l

O <U z s

u

C/3

13 <

c/3

c

<us fQ

o

c

c

o

p «

o •'2

O

cc

o

E w

o ^2

>5 cq

+ -c

1

3

o « O «i5 13

a

X

u

r5

§

S2a e

SI <«

o

•4-* 3

|8

u

U

I

a

w2

c

n

II

3^

c

a

(5

3 o G

00 <u

a> u.

>. <u

e

«

ate

«

60

Si

op

"S

I

3

<

§

u3

a

i

I

o

a

a

o-»3

W

Uiu

Si 3

B-SO
sIQII iou

002 2§ a

J3 .g § « it

.S TJ

</i 4>

ea

b

ts 8

•a *-

•55 (U

i-i J- "3 " a

>> 9. a a> — 3 « -a ^ fc i3 o

ST E

(ACC ^° a 1 1 8 eI 5 S H CO

•sO

•2

Sia 2 8

II>Co a

cr

.so . 3 §

^1> Pi a Ia 3 o —

'h ^O S 1 I

•55

11

^2

B-51
05

CO

5

oZ

oZ

1/2

eg

c

II

•ac

3

e

<u

.3

e

c

e

3 O

c

« u

>. « < Oh

fi

a

B

.2S

.S

E

05

I

3

<

(5

I

•3S

3

B.52

<2 53

cr o o § ca3

<L>

•aco cr oZ P ^ o u ^

•c: <c "o

<, ST « E

.3a

Q

O 3

•/^

O (T ?^ !Q ^- tH O 1> o 8 o S > V u S 8 a

C3a

t:oQ

a,

4)

.5 o u

h s S 8 £

it33

esSo3 1OH

ns•3

CQ

_o

ats3 <« O. « a s I i.

is

"3

CQ 1

op l

.5

f-i o

oo

.s

VI

Ob,

C/3 cO

OOc

csucc3O § a

4>

O

Si3

t>5

00 2

•c " o s O "3 s i

a- « 3 £

B-54
I3I1gHzouI
C/2

VV

Ui

u

CO <2 2 w

B 2 c

82 8 2

C/5 «

CI « S31-1 n

S3

u .g

,2 C3 0 is

(i>

U

Oe

ei3 ^

wi_ 5 8 g> w) ^ ^ -I 8 c .5

4) 03

"t? c

52 o

t3 .2

3? «

Oh

g 0 ex c i2 1 8 S m 8 2 2 o

<L>

C 'It i ? 3 § a CM

.b .b tS

r- F- .S

.5 .S ti^ ^ P

i2 ^ c 13

C

<u3

CMO

4>aa,c c

.2

.a « I ^

03

OS fc^ O 3 I

T3 .s P 6 o 32

•c«3

•3a53

"Sbu

'a

e2

T3

SQoH3 (« a.a s o g

e2

S;

0)3c

'3Iie

60

.Si S o

60 .S

1/9 li

03 &H

C/3

•EO

60cocB§

T3

03 60

.s 8

03 -C

•2 .§ S

tt

X o

:§ 2 ^ t> .5 p o t« < O D s

B-56
§s

i28

c5 <4-l

55^ Z1So1zopzoa

X3Oco

x:•«-» §S3Oo

eo

.Scicsc 3o«-»

•a^ 8 8

•aO 6 2J J3s9 au

'3 2 y

t:oac

rt "OucCo

i2 S

^2 "Si

•n « *

fl> -S

a •"2 M a ^2 5 TJ > C b « O (0 b

its

w S I 51

HQ ^ 3 u R

frs

a

[5 iS^^

CO 3

.s3POi3

J!

*-» U3a

•S '2

COc c .a 3c ao

.S3 5 s ^-o o « B u 3 § v> C « <U ^ 5 a3

cr

.5

S/l O

t!

C8

I—I NWOQ

W2o o II .J- « 3 ^ 3 W C/5

rT ^ (N f*^ >w -w

O ^ r-, Z :l ^

B-57
c .S « a, ^ a. M 'S

"S, « , 3 c c o wC/5 2

<2 «

i2 ^ c .1 8:3 s.i

-c

u

00 .5 o 5 IB 2w

•5 ^

6l

2^

4>

O

"a- c a .2 3 § c

^'."2 8

4>3

cr

ii "53 s ^ -a q H .S c o t

as

III to K

C/3

o-I

C3so3 1

:iu

'E.oH

•3

eooa3 <« i i

e2 4i. SCI3

eae1

"S

•2

a>

•aI

00

.sH o

60 .5

C/3

60

.s

•cc

ts sa

<L> T)c3o^ S 2 S

T3 92*-» CO 60 2

c/l g> I a <5I c >. O "35 c o « •> 3

ti5S I i g

,0

60

.3

B-58
a§

.aIo HUoou ?3

tr

<ao

73aCI

•c>3E

•aI8a

X3

i2 •aI 6 -a3oooS a

(2

6X) .s

J3 o 5 a 2aua

•cco

•a

a

K

uu

O Z ^ S at "Zl

^2

B.59
8§5 8

•a88

C/3 C/J

V3gi1

If

f2i i

•a

"5, o

C/3

O5 -3 o 1bw

'5.

f2 »SHS w

a.i i 3ci

eaB1

X)

.3 o

.S

en

.3c3

•coc

.2sau2c3OZ ^2 1

WD 2

•|

Q

a s

.a ^ § B « o

^2

B-60
8 3

EdS51O

09o

.S8

.a

J35c

.S

-ao 5 2 ao

•ac

<ua i

.£3

+-» C

i2a I i

00

.3cs o2 I

B-61
B-62
42 -aIOGOO8

.3

Of) .S

•o

.3

60 .3

*-» 3P iI i

z.Hc

fiHUHZooHuHZouc

Piaa

.33

.3So3

cr

133

T3>

.33

.3a

vo3

.3a

(SI3

.3

^2

T3

4>a

4)

.3 ^

.3

•aI

.3

00 .3

.3coa33

cr

Oh

J3

0<a

T3

<L> Ca

TJi

-aI

T3

.3§u

(1> c2

.13u3

(T

-a3

•O>

.3

J3C

u

00

.3o

<Si 00

T3Uo

13Su

a,ao « .a a|O (i> O G s a p <u§

*-• o o a = 1 S 8 u v W 0) > ^ a o u p G ^ > >/-i o ^

cn -a

(U c « 8

|s

00

n-i ^

I-.

.ti o*-» g a s p c <u 3 PO 8ll Ip

d

Ohi

T3P

Si

00

.33u3

O*

T3

<U 1

T3CD

Ohapco3

cr

C8ap3PCP

TJ5cpo3

9i

ill 8|<2 § I

CO ~ Ca I 6

.3

a)00 < P . p «

.t;

u c ^ > CO

.. 00 00 sec ^ S. S.

ucnp3II

(/3 •^ 3aapo

00

T3

4)P

.3

COa

B<63
2 o2o2o

2:o

•a

<i> 3

IT

u<

<Ua

.Se

<L>

T5

<s

4>co o

a.

9<S

.1 i i- O ifl S .3 s a < G a 2 a 2 o oD a

<u

C/5 09 o

.S >^ c8

•cc12eou

ifS 51 1Ibw

'a

f2 i

oaoa2

pa3

.1: 33

fiQ 1 3a

•3

eao V3Ci

eao1

J3

.s

<l>

4> X3

B-64
o

i

o

P

Z

o

u

o

u

p

z

o

T

3

w

c

o

3

O"

3

O

3

e

o

I

00

.S

§ I

2

3

1

a

c

H

of

"a, s

^3 /i\ IT 1) o a ^

5 O

Vi

4) ^

-»>> .2

3

so

•S

00 O e ^

"> Tt li

o

2 ^

S)T3

3 c c 3

li

2 J

g 2

CX, T3 O

C005

00 aa,

"S s

3

3

8

!|

S §

P 7=;

-a a

3

a,3

o

(U x:

(Up "3

o

00 ^

|.i

1 g

s i

o ^

03 ^

^1

>

B-65
V3u

(Si U

C/5

•c3

cr 1

0H

•cI

Is

.2 8 p i

•a e

•s. f2

la 3mQ

a

•9o

09 II

•c5 §•

60

4^ t O

a e S o

T3 '5 3 8 1

^'i 8 § ^ 2 o 2

•S <fc 2

III? 3

60

.S

ti

.S o I § a *

if

•a c

•S P ^ o <fc .2. 3c

60 ll

ee

'5. « 2

B-66
.S

I

a

g

:l

t

t

o

8

1

4>

2

i

S3

•c

§-

I

•c

2« S

Is

I

3 1

(u o 5-

?^ W5 U 4> 2

a 2 ^ "3 III

o ^

S i ^ n 8

I

2

V J J
0(5 e

§1

55 "T3 O Oi

t:&u o

egG5 i

1.8 o c M U

-1

3.2 S 2 3c8

•a

.3.3s - " 4)

.5 o ^ I8i

(1>

•S S "o

wI—I

(Zl DO

IZI

<us o c i ^ £ -a

60

Si I

W3

(A •cio12oU

if§ I

S51 1

"a! bw

OQt«>3 c

'3I

CQ

la I

•3

fiQe Io

60

.s

f2 05 o

perat Tern 0 o1 1

•3

a,

4> J3

"T '5, * 2

ill

~

4-7

min

per 1

topic

[9] 1

Yes

[10]

Yes

[10]

—

1/1

^ 1

cpu

sec

per

query

term

[8]

2

cpu

sec

for

top

1000

1

Yes

Siemens

—

13.8

to

19.5

[6]

N/A

[7]

Rutgers

Probabilistic

inference

nets

(INQUERY)

Berkeley

~

16

sec/query

for

any

collection

separately

included

in

search

time No No No No No No No No Yes

[4]

Cornell 825/50

to

8568/50

per

query

[2]

Yes

——^

1

Dortmund

632/50

to

1196/50

per

query

[1]

Yes Yes

GYSlEM

NAME

Total

Computer

Time

to

Search

(cpu

seconds)

-

Retrieval

Time

(cpu

seconds)

I

-

Ranking

Time

(cpu

seconds)

|

Methods

Used

in

Machine

Searching

-

Vector

Space

Model

-

Probabalistic

Model

-

Cluster

Searching

-

N-gram

Matching

1

-

Boolean

Matching

-

Fuzzy

Logic

-

Free

Text

Scanning

-

Neural

Networks

-

Conceptual

Graph

Matching

-

Other

B.70
ooCM

(J

(U3T

Ui&c8Iooco

<L>

Oi3

croau3

CT

u.

<L> D.M

•ac83&O>n??

0\ T-H

00 a\ 0\ T-H

CO 00

IT) <N ON

VO IT) <^

r- r- vo (N

II II

r~- in + +

ON NO

8n i-H

)pic; )pic; )pic

,0 (U H Cu o <u CU I

V-13

a) T3

(Uwu

•cu3afOt) c

.21a>

'5, o

ci.

•cu3Uac5c

_o

.si

X) <4-i 1

•a

<up

rutc ruti o 3

cr

Uia.c83

1)3sou

T3C

nju

'So ora

B-71
1

r

1

1 30-200

per

query

[5]

lo

remove

duplicate

subdocuments

and

rank:

8x450

(clock

time)

V)

Soft

Boolean

Yes

NYU

SYRACUSE

Yes

(Stage

1)

Yes

(Stage

1)

Yes

(Stage

2)

Total

time

(cpu &

I/O)

search

and

ranking

is

about

1

minute

per

query

Yes

HNC

N/A

(there

is

no

unranked

document

list

300

seconds

(for

disks 1

&

2)

Yes No No No No No No Yes No

LSI

Can

compute

about

60,000

query-document

similarities

per

minute

when

vectors

are

in

memory adhoc

-

~

10

min.

routing

-

~

0.1

sec

[3]

Done

in

comparison

routine,

included

in

above

time A

modified

vector

space

model

[4]

CLARIT

~4Hrs.

[2]

12

min.

Yes

-

cosine

distance

measure

SYSTEM

NAME

GE

>*

Total

Computer

Time

to

Search

(cpu

seconds)

-

Retrieval

Time

(cpu

seconds)

-

Ranking

Time

(cpu

seconds)

Methods

Used

in

Machine

Searching

-

Vector

Space

Model

-

Probabalistic

Model

-

Quster

Searching

-

N-gram

Matching

Boolean

Matching

j

-

Fuzzy

Logic

-

Free

Text

Scanning

-

Neural

Networks

-

Conceptual

Graph

Matching

-

Other

B-72
i2s

X3 9

.saua

(A I

3^ o O

.s2

Oh

<1> Cu3Co

131

.S

i2o

T3CD>

•ac

.2Cu8ui iIu

•Saoa

00 .91

.a o

.332u

J3oo3

C8i IBi

8-> O a S

<N

B

.S « 11

43 ^

•I I. 3 cr ^ u o o

B-73
5

min

FDF

search

time

per

topic,

~4.25

Hrs.

for

all

50

routing

topics <1

sec

Yes Yes Yes 1

TRWl

Individual

topics;

270-

11,000

sec;

Average

topic:

3300

sec 0

[8]

Yes Yes Yes

21

Yes, See

IB

SEC

Yes

P

Mean

was

about

84

sec

[7]

Included above Yes

CllRI

3-10

sec

depending

upon

selected

parameters Negligible

[6]

Cosine

rule

using

tf

idf

weightings

Retrieval

of

pages

of

text

rather

than

whole

documents

From

less

than

1

S/&C

to 5

sec

[4]

0.05

seconds

total

[5]

Yes

ERIM

Ranged

from

12-24

hours

for

running

all

queries

[2]

Typical

times

were

from

1500

to

2000

cpu

sec.

Yes

[3]

U Mean

was

84

sec.

[1]

Included

in

above

Yes

SYSTEM

NAME

Total

Computer

Time

to

Search

(cpu

seconds)

-

Retrieval

Time

(cpu

seconds)

-

Ranking

Time

(cpu

seconds)

Methods

Used

in

Machine

Searching

-

Vector

Space

Model

-

Probabalistic

Model

-

Ouster

Searching

-

N-gram

Matching

-

Boolean

Matching

-

Fuzzy

Logic

-

Free

Text

Scanning

-

Neural

Networks

-

Conceptual

Graph

Matching

-

Other

B-74
4> •c

<oa

cr

60

C3

.s

GO .s

X)

.3

.3

•sS

00

.3

TJ3

.3co

00e

43

J2o

Si s

13p

8- 1 o

2^ S S O

00 _o

*i '5 °l2 -a§ I 

»« &

4> Si

.3 «

to

u 5

'5b ^

crcouau

J3oI/) 60

.3I

CO3c

"So •co

60cu3

O" 3ao

60

.3

•s

tou

60

H 2 "53 2

60

tn

O 3O .3 3

T3C8

inT3C

CO ?4)3

cr

"cOac8

<L> o00

ao

fin 60 ^ coa

CO60

.3

CO XIo

•6b o

60

.38a

,.0 ~

a.

T33aCO

(L> 8 2

2^8

00

v>

COa

CO

<ua

J33

cr

COao3

COC 3

•s

a3cco

v>c o

COa

"cO<U

CO3

cr3

•o>a3

cr

<i> 60

60

CO

"60 60 i2 2;

TJ

CO

V)

60 .3C

COUa

-5

CO2acu

CO

l-> 3o c

CO , , ^ ^ ID 2 a^u O CO 8

'ao

00 o IT) 00

rt

O r-l

r- o 00 00 t

\q 00 t-^ r-; r-; o ^ CO

in r~ r~- 00

T-H rH 0\ 00 O "-^ O O

rH

O ^ T-I 00 fo p p T-H O >-H T-H r-5

<N T-H \0 \o

O 00 <N rj <S

>n 00 CO

<N o ''•^

f<-i sr (-0

00NN c

CO , , ^ ^ ID 2 a^u

(SI •ac

CO

.aIoino O CO

Srs 00 10 _ ON t-H 00 fO in CM

OO t-ji-ji^Oxvoinvooooq

fNfNCJ^'-Hinfotorj

oo\otnooc<i(Nr-vor--(SO

in'*«s<NOc4'-Hinco'<:tc<i o CO

V-)

n

1-5 r-5 T-H

O

m (s 00 o to 00 in 25 CN 00 O O "-^ 1-5 o

r- 00 T-H CO

T-i 00 r~ 't r- o T); \q vn i-H o\ O O O 1-5 1-5 1-5 c5

^*Ocscoco'^C5i-H<nr~fO *CO^*U2UivOI^CMi-iOO —I ^ ^ __1 _^

CO

r~ cs>

rj- On 00 r-~ 88

00 r- o o o 00 O O CNl CM 00 p o) j-i \p JO ^ p\ \b \b o 00 'J- Co CM <N CM On r~ >n 1—' 1—' 1—' , 1—' o

B-75
Q P I °

.5 5 c . - g .2 S 2 S § I

I—I D

45 cu 5 a3

8- 5 ovO

.s I 5 5 1

*5 W3* 3

Hi ^

§".3 I I o .5

V3 3 <A

•acI

13>

•c

05

T3C3c

c>5 ia

llI s

•Big « -o b

.3 5 0

e3a1/5oI

60

.3

•§

1/3 c

•5b

00cot

13i8 <u Si

B-76
1IIQIaI§i I o

IHu

•oc

•a.a

13 •o

:^

<u 43

.a

<2

4)O c o

Oh ;<

5b 8 2 « « oB

O

B-77
ON

C/3 3 o

cr Pou

S260

p5

Pia

-5 -P a ?5 E6ca

egoua c3

<L>

C

<U3

(T

(U

ki

4-* Ca3u

C/2

O

2; o •o.S o o2 o o o o o oZ oZ oZ oZ

i3 ^ oZ ooO

2>c3 OZ oZ oZ oZ oZ O2a

J3Caa3oQ

S2ucaa c

<u3

rr

a>u

00 Z c3I

C/5 30

B-78
o

•QiaS

.s118au3

cr ca

•c

J5

C 5 oui8§3

O"u8a

<u

fc3oI

-§

•5b

t«u

'Scu3

tr

<ua

T3C

.a

13o3> o

15uc

•aI 3

a>

03

•3 "S J3 _ t-i aj

—sO c

.Sa

.2 5 I

OX) 'Soo

-gcu

•f

(i> "H 13

.sOIu3>

2>Iu

.3i

<uco c

<L> cua38of8

J3a3a3

(U

5^cuIaO3

OS ?

T-H <N c<^ Tj- \n >o, !>, pp. P'. ,—. o

B-79
u

on

S ^ a 2 I c S o <u

•S « o a 3 5 3 -a ^ ^ i

eg

CO c

.2

60

(L> <l>

•C

3 Q

P

o

u

o

n

u

O

u

3 ^ ^ 2

2

.3c

•a

.3 ^ S

D 2 a-

.3

S!

I

.3

> 3

.3 ^ S

2^ :r g D 2

c

s y -

(U J3

a-

>^ s

IT) O

D

j2i

8

<«

<1>

o

c3

cr

4>

t2

C

e

3

<l> a £

18uc

<uO0

c

u

a

3

.3c

o

o

3

U

5 ^

Soc

c

ua

3

o a

c

3

u

IB

o

3^c

o

to Q

3

D

a

B-80
.s§1 3s13

55o

a,E

a,8aoocuuCC

I/) c/:i 3e

00 .s

-Sse

•nu

•c3Eca

13ceoc

"ae8c3OC

k»

i-a

Hi 00 00

173

-*-» w

Vl(U

B'81
1

.a

I s

2

esc

c « S

2 •§ 3

-a ^ e

13 i2 5

e

u

3 .§1

O e« Z <2i

i2 5 3

S 3 ST 2 .S -3 £

c „

^ i

U

U

W3

> k K

o <H _e

E

u o CS §f|

"S :s 1 c

S 2 - c

•£ -§.0 o feb.S i^ 2 §

g .S * E

•£ || §

S8 i o «

CQ ^ 5

a.

c8

e CD §ii

E 2 I c £ .2 ^

z

a

V

3

g"

ha

|2i

E3

Q

.Sa

o

;3

'wO

a.

U

u«

e

.2it o.E

s

u

3

2

•Sc

o

eV

3

2"

U.V

(J! •sI

8

§

.aQ

B-82
on9

P

o

u

o

»

O

5 .S

O

o

C

f2

4>3

o

I,

5 GO * .s

oS .s

3 T3 f i

o

(L> O

O

.2

oZoZ

oZoZoZ

a6

c

u

3

cr

o

3

ai-i

o

B-83
8

S ^ CQ

o

1/3 (Z)

c

z

O1—1

n

u

3

Pi

C3

(Z)

C/3

(1>

PQ

I

E6

CM

Oh

1/3

T3C

3

a

00

C/3 <

i

O

?5

P5

pa

CQo

I

o

o

D

B-84
co§

.sP

1338

B-85
o

1/3

(X

C/5

- > I

I

USa

CO

C/5

CO

Z

O

H

?5

2

a.

W3 5

PQ n PQ J

S S

S <s ^ vo (§)(§)(§)

t-H l-H <N

ii e|

ii u

it Ml« 1

1

CQ

c Pi

B-86
« 4>

CL, u 1

3 a a

o ^

w «o

S S

C/5

PQ

I

PQ

U ^ 2 o

S PQ c

8

V)c c

0 o 1 i

11CO ea a. a.

.S S

e *'

en

cn S

.3

o

e j> s I

li
inO

§

n

oSI

^6

o

aCO

PQ

?5

c

.o

1

S9S

60

on •o

a efl «

o ^ « c .S "o 3 ^

- e i Q S

3

c

I ^

I a

03 «

o- a.

1/3 1/3 CQ CQ 6

o o

O

.sO

g

U

O

8

0
a o

.2 ° I ^

=2 2 d C Q, O « w H (U

.a

PiSI 12 .3

4> a.

B ^ ^ o O

>^

O 3* <u >-< tx _ e

(8 r4 ^ o « 2

<l>

u

-g .s

,9 « ^ c § 2 O 5! Su § a 2 -2 g -S

</3

W

15 ^.

2S e^«J2 *5

a2 «

55 -a S 5 a « « X O

t:

C3

O

Ui t2

15 o 2 5 «^ D W IT) (T C1h<-i

-a

PQ S 1

lb " >»

M

w ^ CO Q. 5 c 2 s F ^ c Nw s 3^"Si- y <u S « ^ 2 w -S £ o E S i2

4>

C

2:1 o c D 4) O« « O f 3 S 8 13 a

a9a

t;

J2

Si.

05

<1> JS

J3

ts

g s o

.2 6

1)

TM 4> 3 P I

(SO c 2 a

3^ a.

l>Q 1

;S .3 I a W 3 « O y ? 3 - ^ 5 W 3 w j= o

4) >-' > « Q «

B-89

3 fc - 8 a s a a 2 a a .3 E

« * u . I S3 "S o -2 ^ ^ « _ S3

.« « c <J 2^ » I 2 S

-8 ^ S " ^ «fl '-o a- 6

C/3

(/3

p j3 •c^ 60 3 .2

:§ ^ 2 ^ ^ i

.1 1 ° . ^

S ^ ^ 2 § « -6 ^ I g D, 5

I •§ H - 1 i

§-2

" .S .3 a

.S ^ 2

3 5 S? S? g

a H 2 fe^ « ^ 8

|5g § •5 O 60 a

•a3o

603

e. u

"3 T3

•S .!« ii

i IS8 CO S ?

« o

I

2^ J3 3 3

.a

s z < a i5 tu

i2 ^ 3 J3 • p .ti "is

§

e ^

u .5 « 9 4) iS T3 O <U

<7S 3

O

Oh3

2 8

c

o "Is

73 p « _

a p XT o ?2 c >

8

a f3

;^ .52 13 S 5 I D,

a =3

2 533

uK 3

o

O

§ 3 . -SS 2 1 S i

O S 3 >

C 3 r\

. 3

M Q

Ui s c 8" a

^ g 2 = g> a o ^

P 73 <^ « 5 H

0)2 «

3

60 U

•S :2

.5 3

S ^ S ^

s

° * g

3^ « a .2 O 55

x: « J> S g <u Oh D- i; cl, g j:

8-91
o

4>oIcoI

JSio

•8ou

ft!

.s §

cj ^ 1 ^

TJ 1-. "§) 1 r

3d

C

.a .S^3s s B I a

1« S

*> § ^ ^ a as1 e -a i s §

11^

•Ss « <S o a 2y

.03 .2 ^ O « o S «

"-^ 9 ^ « 3 3

.s

a itf C <U 43 U T3 -

'I i!!

•S 5 S (U Uj ^ rt _ T3 z: 3 U 3 a a «i

25 s o a.

Mi« 5 "

lis a ? 2i S 2 «

i2O

u

a> o ^ « -6^oM 1.1 S -a c * > <u 3

T3 <*- c « ° a § If

ill C o —

<1> i ^ s ca I

wooj= > 3 C .a ^ a g"-? g o = -9

(2

a ^ 03 ^ C ^ .S 2 "c 5 a §3 f 3 'C 8 V ~ o i *-• a a S o o .a > 3

•c «

!i

l§E ^ S ''ac

<Q en C 4> a I

If

If

(U ^

CO ^§

1:1 ^ 3 b =^

(U U) > C

<u •? 5 2

0.§ I i = 8 «

13 -ti .9c ^ H c U c y a

<u s <u MlS: S2

TO 4>

M

_ 8 a « 5 3

?5 ^ 2 a ii sCO s -a

i2 ^ a t5o«<-c

60 s .s a
8 3 U

C

!/3 '35

UWC/5

O< O 3 Q) c 2UD I £ e

0) >" E H

U S ^ -So w c iS to -5 i2 - K n» Ui fci X X o

to J2 S

5i 5 .5 Z S

i/i ^ o o «

cr c

P< o . O 2 .t; E I ^ 5 ^ U

03 -IT « — |g O S ?i «

T3 03 « 3 ^ I -s ^ c _ "o 5 03 « o I? > C > « o

>^ ;S 5 3 ^ ^ I

.si

1^

•S 2 CO o E ^ co o « £ <l> 3 p ^ E 2 o n E ^

03 « cEI^ So oc

ii«

J2u

_"S

•ac

03co ^ 3 . C <J E « E a>

05

C 60 O C

1^ «

03

PQ

OU3 S 3

<U o "> M ^ A\ 95 < o E -o « ^ a - 3^ ^ -o 5* <u VI 5> --^ w -3 £ ^„ 3,5 8 e 5 Eu

—

T3 E D, « o o S 6 e g I > 5

4>

tfc- 3 U3 O « g 13 a, S U o .2 g (U -o H TD 03

03

O

.2 * 5 E « to N ^ •52 > a £

0£

03 <U 3 <U .5 E ^ 2 E " § 3 c i2 C .S 3

CS 'm

O o o c

0300 . S C

•5

D

03 E J=l ^-1 3 E uc Si

(A

03 -5 (U

(U « 03

ui Cl,

a

•J wi

C

(u 5> o 8 A o ^

03

ag S 8

1)a

O c o

X)O 3 u

15oo

03 ^ 03 5 3 o c B ^ E _g ? 3o +<

03 03 X>^<

•O

A

CO o 2

JO ft O8 ^ ^ ^ A + ? A

ao

'00 c 9 "C

4> 5 3^ <o D,

to ,0

CQ

CO -O 2 " « o C 3 y O £ w CO S< i-> 5 Ji ^EfcS £ 2 to 3i 3 «

03

CO

u3

UM ^ =1 c c E

CO 'K 4) 4> =5 S to

B-93
O

O § 2 c aa 1.^ > :S 2 «

O, 38 p a) > J3 C ^

4> fei « is

ee CI, U CD

1> (O c x: • <u a o

(U oO 52

ODD <U D

X5C

C > W

3> (U

D

C J2 (U i a •

*i X!

J

60

q E s o O

0) <U

l-< (H § a, B e ° 2 13 3 > o

03 j3 8 c g 2o

l-c ooc

01 ai 2 a

)_ CO

IS « S 2 ii

<u

a « 5 3 . . 03 .3 3 O cn Oo .S § c a aaao

eo

uc -r:

•s ^ a 2|cH o ^ G ^ rt\ —? L- 8 « « c ^ ^ U 1) « 5 5 ^ o 5 o H 2 t

00

O3 Co

03 o C C a -o . <U (U o

"O

u '-' n <u |1 e

<U 03 c .S ^ XI a « t- 1 o I o- o „ „ 1- ^ fc«

<L) <U <U 3 0) r: 03 r:

0) > u

CQw

m

O <S1 B-94
9

O (3 «S <I> e 6 ^ s

- n 4> «

O

.s a c vj .2

o6 o

§ i

S i

go in

2 8

§ 00 g o

:2 ;^ 8 q I s S

w

C/3 o

«4-l <1>

2 5> •c .5; y a> .3 1^w . <u 2 e

C/3 D, ta

<1> c

o s i

o

<u *-•

&0 TD

Ef §

J2 <ti

3

o ^

<i2

8 S .S ?

>^ m o 2^ S

o «

« 3

« n e

5> -sa tfi ^

O

I

.3

3 «

CO §

It

« §

:S S

2

tJ >

•a E §

g

r \

S GO I

f~' Sfi a . o «

.5 a ^ ^ -is

•S S .3

60 S> C <*- •P ^ 2 g

°

c ^ u 3 « « ^ > M C8 2 3 E 6

V3

"^21

60 ^

g .3 ^

ha c 5

>..3

<U 3 M

3 -a P

3 t« 3

I i

CO o

as O c

4^ -a

ta 3

1.2

n a

"

Q B> (U ^ 3 2 2P 1m -tJ B

3 S)3

Si :> §3 Ou 60 6 X)

;s i

I

^

a -a

12 ^ 2 a

U S

M B

Q a

3

3

11^S 2

60 .33 M

•5b

o 13

o £3

B

a . n 4>

u is

1 <2

2 2

3 e- 8.§

J3O

3

C

60 a

a

1 1 III

I

« 1^ >.

B 3 0) (U -C

<« P H

* 3 ^

.4>

"

lil 60«

.3 « a ^ I S ^ a ^

•U.S. GOVERNMENT PRINTING OFFICE: 199A-300-568/03020 B-95

ANNOUNCEMENT OF NEW PUBUCATIONS ON

COMPUTER SYSTEMS TECHNOLOGY

Superintendent of Documents

Government Printing Office

Washington, DC 20402

Dear Sir:

Please add my name to the announcement hst of new publications to be issued in the series: National Institute of Standards and Technology Special Publication 500-.

Name

Company ____^

Address

City__ State Zip Code

(Notification key N-503)



Technical Publications

Periodical

Journal of Research of the National Institute of Standards and Technology -Reports NIST

research and development in those disciplines of the physical and engineering sciences in which

the Institute is active. These include physics, chemistry, engineering, mathematics, and computer

sciences. Papers cover a broad range of subjects, with major emphasis on measurement

methodology and the basic technology underlying standardization. Also included from time to time

are survey articles on topics closely related to the Institute's technical and scientific programs.

Issued six times a year.

NISI

Nonperiodicals

Monographs — Major contributions to the technical literature on various subjects related to the

Institute's scientific and technical activities. Handbooks — Recommended codes of engineering and industrial practice (including safety codes)

developed in cooperation with interested industries, professional organizations, and regulatory

bodies.

Special Publications — Include proceedings of conferences sponsored by NIST, NIST annual

reports, and other special publications appropriate to this grouping such as wall charts, pocket

cards, and bibliographies.

Applied Mathematics Series — Mathematical tables, manuals, and studies of special interest to

physicists, engineers, chemists, biologists, mathematicians, computer programmers, and others

engaged in scientific and technical work.

National Standard Reference Data Series — Provides quantitative data on the physical and chemical

properties of materials, compiled from the world's literature and critically evaluated. Developed

under a worldwide program coordinated by NIST under the authority of the National Standard

Data Act (Public Law 90-396). NOTE: The Journal of Physical and Chemical Reference Data

(JPCRD) is published bimonthly for NIST by the American Chemical Society (ACS) and the

American Institute of Physics (AIP). Subscriptions, reprints, and supplements are available from

ACS, 1155 Sbcteenth St., NW, Washington, DC 20056.

Building Science Series — Disseminates technical information developed at the Institute on building

materials, components, systems, and whole structures. The series presents research results, test methods, and performance criteria related to the structural and environmental functions and the

durability and safety characteristics of building elements and systems.

Technical Notes — Studies or reports which are complete in themselves but restrictive in their treatment of a subject. Analogous to monographs but not so comprehensive in scope or definitive

in treatment of the subject area. Often serve as a vehicle for final reports of work performed at NIST under the sponsorship of other government agencies.

Voluntary Product Standards — Developed under procedures published by the Department of

Commerce in Part 10, Title 15, of the Code of Federal Regulations. The standards establish

nationally recognized requirements for products, and provide all concerned interests with a basis

for common understanding of the characteristics of the products. NIST administers this program

in support of the efforts of private-sector standardizing organizations.

Consumer Information Series — Practical information, based on NIST research and experience,

covering areas of interest to the consumer. Easily understandable language and illustrations provide useful background knowledge for shopping in today's technological marketplace.

Order the above NIST publications from: Superintendent of Documents, Government Printing Office,

Washington, DC 20402.

Order the following NIST publications — FIPS and NISTIRs—from the National Technical Information

Service, Springfield, VA 22161.

Federal Information Processing Standards Publications (FIPS PUB) — Publications in this series

collectively constitute the Federal Information Processing Standards Register. The Register serves

as the official source of information in the Federal Government regarding standards issued by

NIST pursuant to the Federal Property and Administrative Services Act of 1949 as amended.

Public Law 89-306 (79 Stat. 1127), and as implemented by Executive Order 11717 (38 FR 12315,

dated May 11, 1973) and Part 6 of Title 15 CFR (Code of Federal Regulations).

NIST Interagency Reports (NISTIR) — A special series of interim or final reports on work

performed by NIST for outside sponsors (both government and non-government). In general,

initial distribution is handled by the sponsor; public distribution is by the National Technical

Information Service, Springfield, VA 22161, in paper copy or microfiche form.
U.S. Department of Commerce

National Institute of Standards and Technology

Gaithersburg, MD 20899

Official Business

Penalty for Private Use $300