I.

Automatic Document; Retrieval Systems

The conventional library classifies documents by numeric subject codes which are assigned manually (Dewey
decimal aystemf Library of Congress system).

Cross-indexing

is provided in a card file by "subject" title, and author.
Both the numberic index and the "subject" cross-indexing
may be inadequate for retrieval.

A book on the intersec-tion of two subjects (e.g.9 "The Aerodynamics of Birds")
or on a new subject (e.g., automata theory —is itmathematics, computer science9 logic?) is hard to classify
and therefore hard to find, unless the librarian is asked
where he filed it. A fully automatic system must duplicate the function of this librarian by extracting information from a natural language request and retrieving
precisely those documents most likely to be needed by therequestor.
One method of subject classification that is usedin automatic retrieval systems assigns to each document
a list of subject identifiers, often called "keywords".
This list can be treated as a binary vector by associating
a position in the vector with each possible keyword in theretrieval system.

The value in a vector position is one

if the associated keyword is assigned to the document described by the fector, zero otherwise.

Retrieval systems

operated by NASA and by the National Library of Medicine
121O/edlars) use this typo of subject classification l J. In3

4both of these automatic retrieval systems, keywords are
assigned to documents manually by subject experts*
An extension of keyword indexing represents eachdocument as a positive weighted concept voctor rather
than a binary vector*

Bach classification concept isweighted to indicate its importance in the document.

Inthe SMART retrieval system* these concepts and weights are
assigned by automatic processing of the natural language
text of each document or abstract.

Lyj

The user's query in an automatic information retrieval
system can take several forms. The user may be asked toformulate his query using a restricted language.

This

language usually includes the set of keywords defined forthe collection and sometimes the Boolean operations "and",w

o r % and w not w .

In the NASA system the user can assign

values to each keyword instead of using the logical operations.

Cross-referencing and hierarchal relationships

among keywords can be used in both the NASA and Medlars
systems to refine or expand the user's initial query*
Both systems require the user to understand the indexing
system in order to formulate effective search requests.
In the SMART retrieval system* the user is asked tophrase his query in natural language*

The query is thenprocessed in the same way as the document abstractv and aquery concept vector is created.

Logical relationships are

not used in the query analysis.
SMART provides a fully automatic information storageand retrieval system of a relatively simple form*

Document

5>
abstracts are analyzed to construct representative concept
vectors which are stored in the computer• ft hen a user types
a natural-language request into the system, it is converted
to a concept vector representation in the same manner*
Several types of automatic text-to-vector conversion havebeen used with the SMART system i>J*

A list of common

words to be ignored in constructing the concept vector ia
provided.

A suffix dictionary is used to reduce nil words

to word stem form*

A word stera thesaurus which treats

each distinct word stem as a concept in the concept vector
is used as an experimental standard*Frequency character-istics may be used to eliminate some concepts ("partialstem thesaurus)v for examplef words occurring less than
five or more than 100 times in a given collection may beeliminated*

This study uses a thesaurus which was con-structed semi-automatically for the subject srea of aeronautical engineering*This "regular thesaurus" recognizes

synonyms5 that is, it converts words of the same meaning
to the same concept, providing better retrieval performance
than the stem thesaurus L «The degree of relationship between a query and a document is determined in the SMART system by some "distancefunction" of the query and document concept vectors*The

most effective of the distance functions tested in CHART
appears to be the cosine correlation, which measures theangle between concept vectors in n-dimensional space l%yj.

The cosine coefficient of two concept vectors ranges from 06to lf and is found by the formulan2ri sicos (rfs) •rXn0n0./s ? 2 •*V
11
The process of determining the relationship of eachdocument in the collection (or some subset thereof) to theuserfs query is colled a "search" operation.

The distancefunction is used to assign to each document a correlation
coefficient indicating the relationship between the concept
vector for that document and the query vector*

The document

identification numbers are then ordered by correlation
coefficient and are assigned ranks from one to S (number ofdocuments being searched) for evaluation purposes•

The doc-ument ^jost closely related to the user's query is assigned
the rank

1(considered the "highest" rank)*

The retrieval algorithm is the goal of any information
retrieval system*

For each query* the system must produce a

set of documents relevant to the requestor^ need*In theSMART system the retrieval algorithm can be varied experimentally*

The retrieval algorithm applies the search oper-ation; it may select subsets of documents to be searched,and it may conduct several search operations in response toone user request*

The details of the retrieval operation

are the primary concern of this study*
One of the simplest retrieval algorithms* here called
the "full search99 algorithm* performs one search oporation

7
using the entire document collection, and selects lor retrieval the highestndocuments in the ranked list re-sulting from the search operation*
each user could select thisan experimental parameter*

In an operating systemn; in the S.VAHT system

nis