Recommended Reading for IR Research Students

Alistair Moffat

Editors:
Justin Zobel

David Hawking

The University of Melbourne
Melbourne, Australia 3010

RMIT University
Melbourne, Australia 3001

CSIRO ICT Centre
Canberra, Australia 2601

alistair@cs.mu.oz.au

jz@cs.rmit.edu.au

david.hawking@acm.org

The Strategic Workshop on Information
Retrieval at Lorne (SWIRL 2004) was
held in Lorne, Australia, from 8–10 December 2004, see http://www.cs.mu.
oz/~alistair/swirl2004/ for further
information. A total of 38 internationaland Australian researchers and Australian
graduate students took part.
Prior to the workshop, participants at SWIRL were asked to provide two nominations, with supporting argument, for a corpus of“must read” papers that IR graduate students should be familiarwith. Attendees chose papers that represented key breakthroughs,
or represented work undertaken to a particularly high standard, orthat presented established material in an innovative or accessible
manner. The results are, we believe, illuminating and thoughtprovoking.
The IR community is fortunate in already having an excellent
compendium of past research, the edited volume Readings in Information Retrieval (K. Sparck Jones and P. Willett, Morgan Kaufmann, 1997), which not only contains many interesting papers but
has detailed commentaries on key sub-areas of IR. However, it is
becoming dated; the most recent papers are now a decade old. Another benefit of Readings was that it made available many papers
that, at the time, were otherwise difficult to access. Today, almost
all of the papers nominated by SWIRL participants are available
online – as indeed are most of the papers in Readings – and there
is more value in compiling a list of recommended works than inproviding the works themselves. We hope that our annotated reading list, admittedly a much less polished production than the earlier
collection, provides a useful update to Readings and a valuable resource for graduate students.
Of the nominated papers, only five received more than one vote.
The distribution of nominated papers by year is roughly skewed
normal, with a median of 1998. The skew is in favor of recent
papers. Only four venues provided more than one nominated paper:
SIGIR (16), JASIST (4), IPM (3), and SIGIR Forum (2). However,
TREC work featured prominently.
Each of the “commentaries” below is a contribution from a single
individual; thus some papers have multiple commentaries. Someof the entries have been edited to reduce their length, while staying close to the author’s original intention. Papers are listed in order of original publication date. The various commentaries were
contributed by Vo Ngoc Anh, Peter Bruza, Jamie Callan, Charlie Clarke, Nick Craswell, Bruce Croft, Robert Dale, Sue Dumais,
Luis Gravano, Dave Harper, Dave Hawking, Bill Hersh, Kal Järvelin,Gary Marchionini, Alistair Moffat, Doug Oard, Laurence Park, Edie

Rasmussen, Steve Robertson, Mark Sanderson, Falk Scholer, Alan
Smeaton, John Tait, Andrew Turpin, Phil Vines, Ellen Voorhees,
Ross Wilkinson, Hugh Williams, and Justin Zobel.
Probabilistic models of indexing and searching(S. E. Robertson, C. J. van Rijsbergen, and M. F. Porter, SIGIR,
1981)
Commentary: This paper forms a link between on the one hand,
the ideas on statistical indexing of Bookstein, Swanson, Kraft andHarter, and on the other hand, the probabilistic view of searchingof Robertson, Sparck Jones, van Rijsbergen and others. The modelof Harter et al. included a specific relation between a semantic notion (eliteness) and observable statistical data on term occurrence;
this gave a handle on how to make use of within-document term
frequency in the searching models. The paper starts with what I
still think is a good way to develop the basic probabilistic searching model. The methods developed and tested in this paper were
not in fact very successful. However, a considerably simplified
version of the model was the basis for the Okapi BM25 scoring
function, developed some years later (SIGIR 1994). The Harter 2Poisson model can also be seen as a precursor to a simple language
model. A further paper covering the development of the probabilistic searching models, from the binary independence model ofRobertson and Sparck Jones through to Okapi BM25 is the two-partpaper by Sparck Jones et al. [2000].
Term-weighting approaches in automatic text retrieval(G. Salton and C. Buckley, IPM, 1988)
Commentary: While this paper is old – it uses only small collections, and good document length normalization techniques andmore theoretically-motivated weighting techniques came after this– it is a classic paper for several reasons. First, the paper clearly
demonstrates the importance of good weighting. Second, it explains the three components to term weights (document frequency,
collection frequency, document length normalization) and defines
the weighting nomenclature of “SMART triples” that still has some
use today. Third, it is a good example of a retrieval experiment, especially demonstrating the need for testing on multiple collections.
Towards an information logic(C. J. van Rijsbergen, SIGIR, 1989)
Commentary: This paper comes out of left field. It recasts the IR
matching problem in terms of inference, instead of matching. An
important point about this paper is that it tries to get a handle on theissue of semantics of IR. The paper and others Keith wrote started
a line of research into logic-based IR. Even though this researchnever led to major pragmatic developments, it allowed IR to beconsidered in a broader light. As IR blurs into areas such as text
mining and knowledge discovery, it is possible that the philosophy
behind this paper will be given a new lease of life.

for interesting (and easy) reading, and sets the scene for what thefield was like (and struggling with) just 15 years ago. Students
new to the field might find the rapid pace of change within halfa generation a sobering reminder of their real place in the grandscheme of things. So, while not really seminal, certainly worth aread, especially to people interested in efficiency.

Indexing by latent semantic indexing(S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and
R. Harshman, JASIS, 1990)

A re-examination of relevance: Toward a dynamic, situational
definition(L. Schamber, M. B. Eisenberg, and M. S. Nilan, IPM, 1990)

Commentary: By using singular value decomposition, the authors
present a method, latent semantic indexing or LSI, to reduce thedimensionality of the original term-document matrix, creating amuch smaller set of orthogonal factors. LSI offers an automatedapproach to indexing which is based on concepts rather than individual words, addressing the synonymy and (in part) the polysemy
problems. Since the publication of this paper in 1990, LSI has
been applied in a range of applications in information retrieval andrelated activities such as development of ontologies, text categorization, text mining and spam filtering.

Commentary: This landmark paper initiated the wave of relevanceresearch to come during the next 13 years. It re-examined the literature made during 30 years, relying on the central works by Cuadra
and Katter (1967), Rees and Schultz (1967), Cooper (1971), Wilson(1973), and Saracevic (1975). Essentially, the conclusions were as
follows. (1) Relevance is a multidimensional cognitive concept. Its
meaning is largely dependent on searchers’ perceptions of information and their own information need situations. (2) Relevanceassessments have multidimensional characteristics; Relevance is a
dynamic concept. It can take many meanings, such as topically adequate, usefulness, or satisfaction. But relevance is also dynamic
as assessments of objects may change over time. (3) Relevanceis a complex but systematic and measurable phenomenon – if approached conceptually and operationally from the searchers’ perspective. Schamber et al. [1990] stressed the importance of context
and situation. They re-introduced the concept of “situational” relevance derived from Patrick Wilson’s concept in 1973, originatingfrom Cooper (1971). Context may come from the information objects or knowledge sources in systems, but may also be part of theactual information-seeking situation. Two lines of relevance research very fast followed the suggestions and conclusions in thispaper. One track pursued the theoretical developments of relevancetypes, criteria and measurements, thereby bridging over to laboratory IR evaluations. The other line of research consists of empiricalstudies involving searchers in realistic settings.

Commentary: IR, as a field, hasn’t directly considered the issue ofsemantic knowledge representation. The above paper is one of the
few that does in the following way. LSI is latent semantic analysis(LSA) applied to document retrieval. LSA is actually a variant of a
growing ensemble of cognitively-motivated models referred to by
the term “semantic space”. LSA has an encouraging track record ofcompatibility with human information processing across a variety
of information processing tasks. LSA seems to capture the meaningof words in a way which accords with the representations we carryaround in our heads. Finally, the above paper is often cited andinterest in LSI seems to have increased markedly in recent years.
The above paper has also made an impact outside our field. For
example, recent work on latent semantic kernels (machine learning)draws heavily on LSI.
Basic local alignment search tool
(S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman, Journalof Molecular Biology, 1990)
Commentary: Genomic information retrieval is an emerging areaof intersection between information retrieval and bioinformatics.
Since the early 2000s, it has been a key SIGIR interest area. The
seminal genomic IR paper is this original BLAST description, a paper cited in almost all bioinformatics papers since its publication.
For those beginning Genomic IR, this is the key paper. BLAST
is the Basic Local Alignment Search Tool, a heuristic approach to
local alignment that is used to compare genomic DNA and protein
sequences. With BLAST, users can search large genomic collections by providing a query sequence and view ranked results. Biologists use this tool to begin almost all explorations of unknown
sequences, and most biologists are as familiar with its output as
web users are with Google.
Retrieving records from a gigabyte of text on a minicomputer
using statistical ranking(D. K. Harman and G. Candela, JASIS, 1990)
Commentary: This paper documents a pragmatic encounter with– shock, horror – a gigabyte of text. But in 1990 a gigabyte wasa lot, and while many of the techniques described in this paper
have been refined, improved, or downright replaced, it still makes

Okapi at TREC-3(S. E. Robertson, S. Walker, M. M. Hancock-Beaulieu, and M. Gatford, TREC-3, 1994)
Commentary: This paper (and associated TREC-3 runs) introduced
the BM25 weighting formula and demonstrated that it worked very
well. Comparing BM25’s effectiveness against an arbitrarily chosen “tf.idf” formula provides newcomers to IR with a compellingargument for the value of well-thought-out mathematical modelsof retrieval. The performance of BM25 (and also Inquery) spurred
an immediate advance in length normalization in the vector spacemodel and has influenced many, many research papers and production retrieval systems. Ten years later BM25, with the original
TREC-3 tuning parameters, still provides a credible baseline formany retrieval tasks, against which new models can be measured.
Collected papers about TREC-2(Various Authors, IPM, 1995)
Commentary: TREC has since 1992 been the single most significant influence on research in information retrieval. It is not an
exaggeration to say that, certainly in the early years, it was revolutionary. It provided a definitive mechanism for separating successful from unsuccessful approaches and provided the first clear

demonstration of many techniques that are now seen as foundational. Some of the papers in this special issue concern specific systems; written in the earliest years of TREC, they are now dated but
they stand as a demonstration of the power of the TREC approach
to identify strong IR techniques. Of particular interest are three papers. Harman’s explanation of TREC is a summary of why TREC
was necessary and of the rationale for its design. Sparck Jones’s reflections on the TREC approach are valuable for the many insights
into experimental methodology. And the Okapi work stands as ademonstration of clear explanation of and rationale for a specific,
successful method.
Pivoted document length normalization(A. Singhal, C. Buckley, and M. Mitra, SIGIR, 1996)
Commentary: This pair of papers (the other one being Sparck Jones,
Walker, and Robertson [2000]) represent a substantial advance over
tf.idf. When Amit unleashed a new approach to length normalization in TREC, we saw a significant improvement, and experimental
evidence why this was the case. When Steve introduced the Okapi
BM25 it provided a strongly grounded approach to tackle this problem – grounded in probabilistic retrieval theory. Together theyshow that by looking hard at the experimental data – not tweaking!
– it is possible to come up with a significant advance on existing approaches, and that by looking hard at the theoretical underpinnings
of information retrieval it is possible to elegantly and efficiently describe and compute such problems. Together they show the valueof deeply experimentally based traditions with more model driven
explorations can combine to provide us with collective insights into
the uncertainties of associating information and need.
Commentary: Much of the work on improving the precision of thevector space method (VSM) has been performed as trial and error.
A concept is thought of and tried out on a few document sets. If the
precision increases, then it becomes the new vector space method
standard for the time. The authors of this article have taken a different direction in improving the VSM. The probability of a documentof a given length being retrieved is compared to the probability ofa document of the same length being relevant. Their results showthat the gradients of relevance and retrieval differ and they meet ata pivot point which is specific to each document set. By normalizing the document-term frequency weights by this pivoted slope(rather than the usual document vector norm) they achieve a significant improvement in retrieval. This work changed the way wethink about document normalization and provides a reason as towhy the change occurred. This normalization method is now usedin the vector space and probabilistic document retrieval methods.
Filtered document retrieval with frequency-sorted indexes(M. Persin, J. Zobel, and R. Sacks-Davis, JASIS, 1996)
Commentary: This paper (and the preliminary version of it by the
first author in SIGIR’94) took up and ran with the idea of structuring the index to handle ranked queries as the number one goal,
rather than Boolean ones. This simple change allowed a range ofefficiency improvements, including dynamic pruning techniques.
Other work then followed, suggesting other non-document basedorderings for inverted lists. Anyone studying IR implementationneeds to visit this paper, as the starting point for a whole thread of
logical development.

Natural language processing for information retrieval(D. D. Lewis and K. Sparck Jones, CACM, 1996)
Commentary: This nomination, like nomination of Hobbs et al.
[1996], is made with an agenda in mind: we need to see more interaction between research in IR and research in NLP. This paper,
written in 1996, was one of the first to argue for a research agendathat explored how NLP techniques could be used to improve information retrieval. The paper was written at a time when the world
was in the process of moving to full text search, as opposed to surrogate search; the availability of indexes built from the full text of
documents, rather than just abstracts and titles, opens up a range of
new opportunities to apply ideas from natural language processing
in order to improve indexing. The paper proposes three particular directions where NLP ideas might be explored: first, the useof parsing techniques to identify the appropriate terms to be used
in indexing, rather than, for example, relying on simpler collocational criteria in determining compound terms; second, the use of
NLP techniques to determine related terms (for example, semantically superordinate terms) to be used in indexing; and third, the useof NLP techniques to implement more sophisticated processing ofuser queries. The paper is a flag waving exercise in the sense thatit suggests a number of directions that might be explored, but it
leaves the research to be carried out by others; to my knowledge,
the agenda proposed has not yet been fully explored.
FASTUS: A cascaded finite-state transducer for extracting
information from natural-language text
(J. R. Hobbs, D. Appelt, J. Bear, D. Israel, M. Kameyama, M. E.
Stickel, and M. Tyson, In Finite-State Language Processing, MIT
Press, 1996)
Commentary: As with my nomination for Lewis and Sparck Jones[1996], this nomination is made with an agenda in mind; we needto see more interaction between research in IR and research in NLP.
This paper remains one of the most cited foundational papers in information extraction (IE). On the face of it, information extraction,
which is concerned with extracting from a document a set of predefined informational elements (typically, who did what to whom
and when), does not have much to do with information retrieval asthat task is commonly understood. Whereas IR is concerned withretrieving either documents or passages within documents, IE isconcerned with extracting specific elements of information from agiven document; IE is widely viewed as one of the more successful
application areas to come out of NLP, and this paper just happens
to be a good overview of the kinds of processing that are involvedin building IE applications. I believe the paper (and the field of IE
generally) is of interest to the IR community because it takes us
beyond simple text retrieval to what we might think of as knowledge retrieval; combined with IR techniques to locate relevant documents, IE can deliver a summarization of essential content thatmeets the same set of needs as those addressed by IR more generally, that is, the management of and access to large document sets
in a meaningful and useful manner.
Self-indexing inverted files for fast text retrieval(A. Moffat and J. Zobel, ACM TOIS, 1996)
Commentary: This work describes in detail how to build a document index for fast text retrieval. It begins by covering the lower
level of compressing sequences of positive integers using gamma,

delta and Golomb coding of d-gaps. It explains how queries are resolved using simple Boolean and ranked document retrieval methods. It then goes on to discuss fast retrieval methods using skippingfor Boolean queries and reduced-memory ranking (the Quit andContinue methods) for ranked queries. Experimental results are
given for query times, storage required and precision of the query
results, showing that the methods provided are useful in building alarge scale document retrieval system. This article covers the wholeautomatic document indexing and querying process and is very useful for those who wish to implement their own system. This work
also forms the basis of large scale information retrieval and should
be read by those who wish to enter the field.
Commentary: This paper provides a good example of research (andresearch communication) into the efficiency of information retrieval
systems, presenting an alternative structure for compressed inverted
indexes that allows fast query processing with small sacrifices inindex size. In achieving the goal of improving the retrieval efficiency, the paper provides a useful introduction to the main components of information retrieval systems: indexing, index compression, and the query evaluation process. It motivates the new compressed index structure by analyzing the operations needed during
Boolean and ranked query evaluation, covering also some pruning techniques that can be efficiently applied for the latter without
degradation on retrieval effectiveness. The paper is very well structured and written. I especially value the presentation method for
introducing new index structures – a combination of motivation,
description, formal analysis, and practical experiments.
Cognitive perspectives of information retrieval interaction:
Elements of a cognitive IR theory(P. Ingwersen, Journal of Documentation, 1996)
Commentary: This is a key paper in the development of a holistic
cognitive theory for information retrieval interaction. It draws froma number of theories of IR and Information Science and synthesizes
them towards a holistic cognitive theory of IR. The theories (or approaches) covered include IR matching models, user-oriented IR
research, prior cognitive IR research, informetrics and information
seeking. The paper discusses several types of information needs(requests) and several levels of their representation. Mainstream
IR focuses on well-defined and stable topical requests while Ingwersen also discusses the importance (and realism) of covering
also ill-defined and variable requests, which may be represented atrequest level, problem/goal level or work task level. Further, thepaper discusses the concept of poly-representation of documents
and suggests that the cognitive overlaps of different representations(for example, from cognitively different origins) be employed in retrieval. Similarly, various request versions and representation levels
provide possibilities for poly-representation. All this is significant
if IR research seeks to be more aware of the variety of situations
and contexts where IR is applied. And it should.
A survey of multilingual text retrieval(D. Oard and B. Dorr, University of Maryland Technical Report,
1996)
Commentary: Cross-lingual information retrieval has attracted tremendous interest over the past decade, spurred on first by TREC andlater the CLEF and NTCIR conferences aimed at European andAsian language CLIR respectively. Yet there is very little in theway of survey papers in Cross lingual, or Multi-lingual information

retrieval. The most commonly cited paper, even in relatively recent
literature is this unrefereed 1996 report by Doug Oard and Bonnie
Dorr. Although Oard has published a number of more recent papers, this one appears to be the most commonly cited because of
the length and amount of detail it contains. Regrettably some of
the information is becoming dated, and underlines the dire need fora good survey paper in this area.
Simple, proven approaches to text retrieval(S. E. Robertson and K. Sparck Jones, Cambridge Technical Report, 1997)
Commentary: My second nomination is something that has neverbeen published (!) but I always point students at. Its a really simple
DIY guide to building an IR system though it is a pity nobody has
updated it to 2004 weighting formulae. I like it because it is simple
and straightforward and something any undergraduate or graduate
student can pick up, read, understand, and then do it. Its somethinga student should see early in life and always have to hand. There
are very many papers in IR which are tough to read and deservedly
so because their material is difficult, but there are very few simple,
classical, starter papers, and this is the best one I have found.
Commentary: This paper provides a brief but well informed andtechnically accurate overview of the state of the art in text retrieval,at least up to 1997. It introduces the ideas of terms and matching, term weighting strategies, relevance weighting, a little on data
structures and the evidence for their effectiveness. In my view it
does an exemplary job of introducing the terminology of IR andthe main issues in text retrieval for a numerate and technically wellinformed audience. It also has a very well chosen list of references.
Many of my graduate students come from more conventional computer science backgrounds and are unfamiliar with the ideas it contains. I think it would provide a very useful early chapter for areadings book.
An informal information-seeking environment(D. G. Hendry and D. J. Harper, JASIS, 1997)
Commentary: This paper describes an information-seeking environment, in which its design is informed both by carefully frameduser needs and by an analysis of design alternatives using Green’s
Cognitive Dimensions Framework (CDF). Information-seeking ispresented as a problem-solving activity, and the authors argue forunder-determined, flexible interfaces to support informal problemsolving practices when searching. This environment emphasizes aparticular cognitive dimension, namely secondary notation, in thatthe user can employ space, layout and locality to represent and organize his/her search activities. Effectively, the authors view theenvironment as a “spreadsheet for information organization and retrieval” (Marchionini), in which the display “talks back” (Schön)
to people about their work. This paper is recommended to anyone
interested in designing information-seeking environments (or information retrieval interfaces) where they are concerned with what
users want to do with such systems, and where they wish to understand the consequences of high-level design decisions on user
behavior and performance. The paper challenges the designer tobe free of the strait-jacket of the classical information retrieval interface (query input, result list, document display), which arguably
limits information seeking activities, and to widen the space of design possibilities for information-seeking environments.

Improved algorithms for topic distillation in a hyperlinked
environment(K. Bharat and M. Henzinger, SIGIR, 1998)
Commentary: There was other early work on hyperlink-based ranking, notably by Brin and Page [1998] and Kleinberg [1999]. Suchpapers tended to include no effectiveness evaluation, or very nonstandard evaluation. To this day, there is a genre of paper that introduces, for example, a new PageRank variant, and completely fails
to test its impact on retrieval effectiveness. Bharat and Henzinger
use standard IR evaluation methodology, but with web-specific judging instructions to identify “relevant hubs and authorities”. It introduces new ranking methods and demonstrated their superiority. It
offers insights which remain true today, into the problem domain
and specific problems of link analysis. Although it fails to compare link-based ranking to pure content-based ranking (this wasn’t
done until SIGIR’01), it is an early and important crossover between hyperlink-based ranking and IR methodology.
How reliable are the results of large-scale information
retrieval experiments?
(J. Zobel, SIGIR, 1998)
Commentary: Together with Voorhees [1998], this paper heralds
the start of a series of papers appearing in SIGIR that investigatedan aspect of IR that had been little examined up to this point, namely
the evaluation of retrieval effectiveness. Researchers had been using the TREC collections for nearly a decade assuming that thecollections were OK even though relevant documents were being
sampled form the collection using pooling. Apart from the original British Library reports from Sparck Jones and van Rijsbergen,and some checks conducted by Donna Harman in the early yearsof TREC, no one had really looked carefully at the reliability of
the QREL sets being produced by pooling. Zobel [1998] provided
such re-assurance demonstrating through the results of carefully
conducted experiments why it was OK to rely on pooling. The
paper is a tour de force of experiments exploring a range of topics relating to test collections, such as proposing a more efficientmethod of locating relevant documents from within pools, but thecore result, pooling is a good way of finding QRELS is the finding
the paper should be remembered for.
Variations in relevance judgments and the measurement of
retrieval effectiveness(E. M. Voorhees, SIGIR, 1998)
Commentary: Until 1998, test collections were often criticized fortheir relevance judgments, people would say that the judgments
were unreliable because almost all test collections were formed
with relevance judgments (QRELS) made by a single person. “People’s judgment of relevance vary, therefore the QRELS of test collections like TREC are unreliable”, people would say. There wassome past work on this matter conducted by Salton using the early
very small test collections, but nothing had been tried on the larger
collections like TREC. Voorhees produced a paper at SIGIR 1998
that answered the concerns of test collections’ critics. Showingthat when different relevance assessors judge documents for relevance, there is a large level of disagreement between assessors,
but the variation rarely changes the relative ranking of systems. In
other words, if System A is found to be better than System B on
a test collection using one set of relevance judgments and then the
judgments are replaced with those of another assessor, on the newversion of the test collection, System A will remain measured better than System B. What sets this paper apart from others is notjust the significance of the result, but the experiments used to produce the result. Voorhees was the first to use the corpus of past
TREC results to test her ideas out and produce a wealth of data
showing that test collections were OK. The Voorhees paper alsoheralds the start of series of excellent papers from Voorhees andBuckley across most of the subsequent SIGIRs, each of which used
past TREC result data to tackle important topics such as stabilityof evaluation measures, reliability of significance measures, all ofwhich deserve recognition.
Advantages of query biased summaries in information
retrieval(A. Tombros and M. Sanderson, SIGIR, 1998)
Commentary: Google’s sweep to dominance in web retrieval ismost often attributed to its use of the PageRank(tm) algorithm. Thisis simplistic: Google brought several new features into the marketplace and each was a key driver in its adoption as the search engine
of choice. One of the most significant advantages of Google – and atechnique adopted by all search engines since – is its query-biased
summaries. These allow users to more effectively judge whether
documents are relevant or not, and also to identify key details suchas email addresses from home pages without retrieving the pageitself. Tombros and Sanderson’s proposal and experimental evaluation of query-biased summaries is seminal IR work. In the paper,
they describe how to compute, rank, and display query-biased summaries, and show they are an effective mechanism for users. The
paper is required reading for anyone building a retrieval engine.
A language modeling approach to information retrieval(J. Ponte and W. B. Croft, SIGIR, 1998)
Commentary: This paper introduced the language modeling approach to IR and has become one of the primary sources for arange of research in recent years. Besides this historical value, thepaper has an interesting way of presenting the material – the waythat might benefit beginning theoretical IR researchers. Step bystep, the authors walk the reader through a discussion of existing
retrieval models, arguing for a move to language modeling. The
new approach itself is described in detail, with motivation provided
for each of the decisions taken. In short, the paper is necessary
for the beginning researchers to understand the language modeling approach as well as understanding many theoretical aspects of
information retrieval research.
Commentary: My first nomination is Ponte and Croft’s Language
Modeling paper from SIGIR’98 and I include this because it wasseminal (cliché!) in that it was the first real exposition of LM in
information retrieval. There was a smattering of subsequent papers
at other conferences in the period following, but this was the firstto a real IR audience and I remember coming out of the auditorium
afterwards and thinking “gosh, that was interesting”. There areprobably better LM papers, and more advanced developments in
LM and other applications, but this one sticks in my mind.
The anatomy of a large-scale hypertextual web search engine(S. Brin and L. Page, WWW7, 1998)
Commentary: This paper (and the work it reports) has had more
impact on everyday life than any other in the IR area. A major contribution of the paper is the recognition that some relevant searchresults are greatly more valued by searchers than others. By reflecting this in their evaluation procedures, Brin and Page wereable to see the true value of web-specific methods like anchor text.
The paper presents a highly efficient, scalable implementation of aranking method which now delivers very high quality results to abillion people over billions of pages at about 6,000 queries per second. It also hints at the technology which Google users now takefor granted: spam rejection, high speed query-based summaries,
source clustering, and context(location)-sensitive search. IR andbibliometrics researchers had done it all (relevance, proximity, link
analysis, efficiency, scalability, summarization, evaluation) before
1998 but this paper showed how to make it work on the web. For
any non-IR engineer attempting to build a web-based retrieval system from scratch, this must be the first port of call.
Commentary: The original Google paper is such an obvious candidate that I hesitated to nominate it, since I’m certain that others
will as well. The web site for the WWW7 conference no longer
appears to be functioning, but typing “Brin pagerank” into Google
produces the paper as the top hit. The fact that the paper can be referenced in this fashion demonstrates its importance. Unfortunately,
it is not very “polished”. Various aspects of algorithms, architecture
and low-level data structures are mixed up together and covered indifferent amounts of detail. Nonetheless, there are few papers thathave had the same level of impact on both research and practice.
It appears as the 63rd most cited paper on CiteSeer, and “Google”
is now a verb. Ideally, a volume of IR background reading would
contain an entire section on web-related methods, including papers
by Kleinberg and Henzinger, along with this one.
Commentary: This paper has been enormously influential for obvious reasons. While this paper does not follow many traditional
information retrieval evaluation and presentation conventions, its
impact has turned it into a must-read paper for anyone interested in
web search.
Commentary: Web search has illustrated several things including
the importance of non-content factors in ranking (beyond “aboutness” to use Hutchins terminology), and of course issues of scalein crawling, index building, and querying. I considered three papers (Brin and Page, as listed; Page, Brin, Motwani and Winograd,“The PageRank citation ranking: Bringing order to the web”, andKleinberg, “Authoritative sources in a hyperlinked environment”).
I picked the Brin and Page paper because of the breadth of topics
it covers. Published in WWW7 in Brisbane, this paper provides ahigh-level overview of an early implementation of the Google web
search engine. It also highlights the importance of non-content
factors (for example, PageRank) along with a variety of content
matches (anchor text, plain text in large font, plain text, etc) andproximity to arrive at an overall ranking. To me, this is the single
biggest contribution of web search systems to the IR communityand has implications beyond the web. Finally, there is some discussion of results presentation issues such as grouping by site andsummarization. There is no systematic evaluation of the ranking
algorithm in this paper, but I still think that the breadth of topics
covered in this paper make it a must read. It’s also interesting to
re-read Appendix A (Advertising and Mixed Motives), five years
and one IPO after that fact. Currently, the predominant businessmodel for commercial search engines is advertising. “The goalsof the advertising business model do not always correspond to providing quality search to users . . . we expect that advertising funded
search engines will be inherently biased towards the advertisers andaway from the needs of the consumers . . . But we believe the issueof advertising causes enough mixed incentives that it is crucial tohave a competitive search engine that is transparent and in the academic realm.” (Kleinberg’s paper is much more scholarly, citing
work in social networks, bibliometrics, hypertext, and so on, but it
is focused almost entirely on properties of the web graph, and inpractice this is only a small part of what goes into building a good
web search engine.)
Exploring the similarity space(J. Zobel and A. Moffat, SIGIR Forum, 1998)
Commentary: This paper exposes the myriad of weighting schemes
for measuring similarity used in the IR literature in a clear, systematic way. It provides a good framework for rigorously testing
similarity schemes, from basic cosine measures through to Okapi
and beyond. It also provides a convenient, succinct notation for describing weighting schemes, removing the “black magic” from IR
engines (if the notation is employed!). To my mind, it puts a stop
to any serious research in the area of fiddling weighting schemesin order to improve recall-precision. Any new changes should beslotted into the framework, and compared with the results obtainedusing the existing schemes as described.
Document expansion for speech retrieval(A. Singhal and F. Pereira, SIGIR, 1999)
Commentary: The key innovation in ASR was that it became possible to create ASR systems that could automatically transcribe
broadcast news, and that in turn made it possible for the first time
to build systems that could effectively search large collections ofuseful spoken content. There were three research threads that deserve mention. The most fundamental was the effective integrationof ASR with IR, which was pursued vigorously by a small number of teams (mostly ASR teams, since IR was the easier of thetwo problems) in the TREC “Spoken Document Retrieval” (SDR)
track. The best ASR systems during this period were built at the
University of Cambridge in the UK, so one of their later TREC papers might be a good choice. An alternative is the paper selected
here, covering document expansion for ASR-based search, which
offers some nice insights into the structure of the problem. The
second thread was the integrated use of ASR with other sources ofevidence (for example, face recognition and video OCR) to search
video. This is presently the focus of the TRECVID evaluation,
but the seminal work in that area is unquestionably the CMU Informedia project because that is the first time that a team had theresources to attack that challenge at large scale. The third thread
was the Topic Detection and Tracking (TDT) evaluations, which
introduced a focus on clustering similar news stories and detecting
stories on novel topics. Score normalization turned out to be thekey to this process, and the BBN team was the first to find a truly
effective solution to that problem. The BBN chapter of the TDTbook edited by Allan might therefore be a good choice.
Information retrieval as statistical translation(A. Berger and J. D. Lafferty, SIGIR, 1999)
Commentary: The paper provides one of the earliest formal treatments of the language model approach to IR. Viewed by many inthe other language technology communities as a seminal IR paper
because it “spoke their language”. It also introduced translation
probabilities into the retrieval model, and this was subsequently
used heavily in cross-lingual retrieval work.

User interfaces and visualization(M. Hearst, In Modern Information Retrieval, Addison-Wesley Longman, 1999)
Commentary: I wanted something about user interfaces for search.
There are lots of individual papers on specific aspects of the problem, but I nominate Hearst’s chapter in Baeza-Yates and RibeiroNeto’s book since it presents an overview of research and innovation in interfaces for search. It covers a wide range of techniques forquery specification and refinement, and for browsing and searchingcollections. Current web search interfaces are about as impoverished as one can get — people are provided with a small rectangle in which to enter the query, shown a list of results, and if thesearch doesn’t return what they want they just have to try again. Researchers have explored a variety of techniques for improved query
specification, results presentation and interaction, many of which
are reviewed in this chapter.
Grouper: A dynamic clustering interface to web search results(O. Zamir and O. Etzioni, WWW8, 1999)
Commentary: This paper is not perfect. I was tempted to recommend the Cutting, Karger, Pedersen and Tukey paper, “Scatter/Gather: a cluster-based approach to browsing large document
collections”, in SIGIR’92 (pages 318–329) instead, but went withthis one as it offers three interesting lessons for readers. First, it
implements a clustering technique that is practical for the WWW
environment. The notion of implementing something that works
rather than aiming for an optimal solution goes against IR goals of
discovering scalable theories, however, it provides a stepping stone
to developing a larger IR environment that actually affects the livesof people. In this sense, the paper demonstrates good engineeringrather than theory. Second, the paper offers an actual user interface
that leverages clusters for search results. The mix of phrases for labeling the clusters, sample titles, cluster size, and query refinementon the interface makes this is very low-tech but high-information
design. I also like the fact that the authors have exposed their numerous design decisions along the way. Note that if the suffix tree
clustering itself is the breakthrough, then the authors’ SIGIR 1998
paper would be a better choice. However, because it is the wholesystem that is the lesson here, I strongly prefer this paper. Third,
there is an evaluation. It is a somewhat novel evaluation in that it
uses a comparison of server logs for two systems and tries to get atsome search path criteria rather than reducing everything to recall
or precision surrogates for performance. They tried some simple
but interesting metrics like click distance within results. One disappointment I have in this work is that, to my knowledge, they havenot done the user studies and follow up work that they say they will
do in the paper.
Authoritative sources in a hyperlinked environment(J. M. Kleinberg, JACM, 1999)
Commentary: Kleinberg’s work on hubs and authorities was a seminal paper in showing how the information inherent in the underlying network structure of the web could be exploited. Kleinberg
bases his model on the authorities for a topic, and on hubs – pages
that link to a large number of thematically related authorities. He
observes that hubs are in equilibrium with, and confer authority on,
the sites to which they link, that is, they have a mutually reinforcing
relationship. This work was significant in providing an algorithmic
approach to quantifying the quality of web pages, a key issue in the

web environment where the massive size of the database, information redundancy and the uncertain quality and source of information make retrieval difficult. Related work (Bharat and Henzinger[1998]; Chakrabarti’s “Clever” system; Brin and Page [1998] andPageRank) has applied similar methods to resource discovery on
the web. (This is actually a second paper on Kleinberg’s work,
the original was a conference presentation 1998. This version has
greater detail.)
Variations in relevance judgments and the measurement of
retrieval effectiveness(E. M. Voorhees, IPM, 2000)
Commentary: Evaluation is an important component of the IR field,
and most evaluation is done using the Cranfield methodology. This
paper addresses one of the major concerns about the appropriateness of the Cranfield methodology by confirming that while relevance judgments do depend on the assessor, the relative qualityof retrieval runs is stable despite these changes. This result holds
for different collections, different evaluation measures, different
types of judgments, and different types of assessors. The paper
also shows that the upper bound on the effectiveness of retrieval
systems as measured by recall/precision is limited by this disagreement among humans, and therefore systems cannot hope to reachthe theoretical limits of “perfect” precision or recall.
A probabilistic model of information retrieval: development
and comparative experiments. Parts I and II(K. Sparck Jones, S. Walker, and S. E. Robertson, IPM, 2000)
Commentary: This two-part paper presents a probabilistic retrieval
model. It begins from first principles, and derives formulations that
culminate in the Okapi BM25 ranking function. As such, it draws
together developments and experiences from over a decade of IR
research. The paper is important because: it explains the successful Okapi BM25 ranking function; a probabilistic model of retrieval
is derived from first principles; important assumptions underlying
the model are explained; the paper systematically shows how additional sources of information (for example, relevance informationand term frequencies) can be incorporated into the model; comprehensive experiments, based on the TREC framework, are included
to illustrate the impact that different parameters have on overall
performance.
Commentary: See under Singhal, Buckley, and Mitra [1996].
Evaluating evaluation measure stability(C. Buckley and E. Voorhees, SIGIR, 2000)
Commentary: This paper investigates the stability of widely-used
IR evaluation measures such as mean average precision (MAP),
precision at 10 documents retrieved, and R-precision. By calculating error rates based on runs submitted to the TREC Query track,
the authors demonstrate that the stability of different measures canvary significantly. For example, MAP is shown to have a low error rate when 50 topics are used. Precision at 10 documents retrieved, on the other hand, has a substantially higher error rate.
This paper is important because: it gives an overview of the experimental methodology used to evaluate the performance of information retrieval systems; the assumptions underlying the commonlyused evaluation measures are investigated; important limitations
are demonstrated, assisting IR researchers to conduct meaningfulexperiments for the evaluation of new ideas; it promotes thinking

about the meaning of the numbers, rather than just looking at the
numbers themselves; a sizeable bibliography of important related
papers that consider experimentation in IR is included.
Do batch and user evaluations give the same results?
(W. Hersh, A. Turpin, S. Price, D. Kraemer, B. Chan, L. Sacherek,
and D. Olson, SIGIR, 2000)
Commentary: It is not often that one would point to a “failed” experiment as a key piece of work in a field. However, the failureof the users in the experiments reported in this work to gain thebenefit predicted in batch experiments is a key piece of research.
Our colleagues in Library Sciences keep on pointing out the importance of the people who use information retrieval systems, and too
little of our work takes into account how people use information retrieval systems. Even enormously successful information retrieval
experiments – Google – still spends comparatively little effort onunderstanding its user’s behaviors, and this paper points out therisks of such effort.
Commentary: This paper, and its companion in SIGIR 2001, areimportant because they provide solid evidence that optimizing IR
engines using batch experiments in the ad-hoc style of TREC does
not necessarily translate into an improved IR engine for users. Unfortunately they do not offer any real reasons why the “improved”
systems do not translate into real improvements, either perceived
or actual. Hopefully these papers will cause some researchers tostop the relentless pursuit of ever higher precision on known query
sets, and concentrate on human factors in the retrieval process.
Content-based image retrieval at the end of the early years(A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain,
IEEE PAMI, 2000)
Commentary: This paper is a comprehensive and authoritative survey paper of CBIR up to pretty much its date of publication in 2000.
It is an exemplary survey paper, and also appeared at a turning point
in its field. In particular it appeared just as really effective systems appeared which focused on allowing the user to explore images in feature space, rather than categorical or analytic keyword
searching. Subsequent advances within feature space searching
have tended to be incremental refinements, not presenting fundamental advances on the work reviewed in the paper. For this reason
I believe this paper will become a classic, representing not just astate of the art, but also the possibilities and limitations in terms of
retrieval effectiveness within a given set of technical limitations. It
really defines what can be done in (especially) still image retrieval
without the introduction of deep semantics or a surrogate for them,that is automatic keyword image indexing. It contains definitionsof such specialist terms as the “semantic gap” and makes some perceptive comparisons between image and text retrieval which alsobroadly apply to other forms of multimedia retrieval (for example
music).
Distributed information retrieval(J. Callan, In Advances in Information Retrieval, Kluwer Academic
Publishers, 2000)
Commentary: This paper is on distributed information retrieval. In
distributed information retrieval, the documents of interest appear
scattered over multiple text databases, which can be heterogeneousalong a number of dimensions, such as topical focus, intended audience, supported query evaluation models, and degree of “cooperation” in the distributed retrieval process. Unfortunately, text
databases on the web are often not “crawlable” by traditional methods, so search engines largely ignore the database contents. Furthermore, these “hidden-web” databases are often of high quality,
which highlights the importance of distributed information retrieval
research to – in the not-so-distant future – fully integrate searchover the crawlable web with search over the hidden-web database
contents. This very nicely written paper presents an overview of
key research addressing the main challenges in distributed information retrieval.
A study of smoothing methods for language models applied toad hoc information retrieval(C. Zhai and J. Lafferty, SIGIR, 2001)
Commentary: This paper is one of the better descriptions of thegenerative approach to using statistical language models for probabilistic information retrieval. However, what I find most interesting
about this paper is that it shows that (i) even this “more principled”
approach to probabilistic IR requires careful tuning for success, and(ii) the basic theory offers little guidance about how to do the tuning. The result is a clean theory, with knobs for tuning, and ad-hoc
methods for doing the tuning. I think this paper reveals both thestrengths and weaknesses of the generative approach to using statistical language modeling for IR.
Relevance-based language models(V. Lavrenko and W. B. Croft, SIGIR, 2001)
Commentary: One criticism of most statistical language modeling approaches to information retrieval is that they are essentially
word-matching models, like the much maligned (although very successful) vector-space retrieval model. There is no place in the modelfor the user, the user’s (unspecified) information need, or the concept of relevance. This paper is the beginning of a line of influential research from Lavrenko and Croft that bridges the gap between classical models of probabilistic information retrieval andthe newer statistical language modeling approaches to probabilistic
information retrieval.
Cross-lingual relevance models(V. Laverenko, M. Choquette, and W. B. Croft, SIGIR, 2002)
Commentary: There have been many papers written about Crosslingual information retrieval (CLIR) in recent years. Most of thepapers discuss some improved technique, together with a specificcollection on which the experiments are conducted. Inevitably thetechnique leads to an improvement compared to the baseline. While
such results do provide information about successful techniques,
they often lack an overarching theoretical framework (a not uncommon problem in IR!). There are a number of approaches to
CLIR, generally using one or more of dictionary, corpus, or parallel texts as resources to facilitate CLIR. The above paper represents
one of the few attempts to construct a formal model for the crosslingual retrieval process, and analyze different techniques withinthat framework. It is also interesting because it does not rely on
any translation mechanism, but models the probability of a document in language A being relevant to a query in language B. The
supporting experiments use the TREC 9 Chinese CLIR task.

Factors associated with success for searching MEDLINE andapplying evidence to answer clinical questions(W. R. Hersh, M. K. Crabtree, D. H. Hickam, L. Sacherek, C. P.
Friedman, P. Tidmarsh, C. Moesback, and D. Kraemer, Journal of
the American Medical Informatics Association, 2002)
Commentary: This is one of several investigations looking at howreal users do with IR systems. I choose this paper because thegeneral IR community is less likely to be familiar with it (as compared to other papers coming out of the TREC Interactive Trackthat had similar findings). This paper assessed the factors associated with the correct answering of clinical questions by advanced
medical and nurse practitioner students. They used a state of the art
MEDLINE system. The research found that a substantial numberof questions were answered incorrectly even aided by the use of an
IR system and that one of groups, nurse practitioner students, obtained no benefit from the system at all. The study also looked at
the relationship between recall/precision and successful use of thesystem, finding there was no relationship whatsoever, giving further
credence to the notion that these measures are not that important in
the larger searching environment.
A taxonomy of web search(A. Broder, SIGIR Forum, 2002)
Commentary: I believe this paper is important because it looks at
the environment most people use to do IR, the web, and analyzes
what they do with it. It is important to realize that IR is an importantpart of using the web, but not all of it.
Stuff I’ve seen: A system for personal information retrieval
and re-use(S. Dumais, E. Cutell, J. Cadiz, G. Jancke, R. Sarin, and D. Robbins, SIGIR, 2003)
Commentary: I selected this paper because it addresses an increasingly important problem – how to search and manage personal collections of electronic information. So the primary reason to choose
it is that it addresses an important user-centered problem. Secondly,
as in the first paper, this paper presents a practical user interface
to make the system useful. Third, the paper includes large scale,user-oriented testing that demonstrates the efficacy of the system.
Fourth, the evaluation uses both quantitative and qualitative data
to make its case. I think this paper is destined to be a classic because it may eventually define how people manage their files for a
decade. Moreover, it is well-written and can serve as a good modelfor developers doing system design and evaluation, and for students
learning about IR systems and evaluation.
On collection size and retrieval effectiveness(D. Hawking and S. E. Robertson, Information Retrieval, 2003)
Commentary: This paper is an exemplar of good research method
in information retrieval. The authors take hypotheses put forward
by participants in a TREC track (VLC at TREC 6, 1997) and devise
experiments to test each of the hypotheses. The experiments are diverse, ranging from obvious tests on effectiveness to approaches
based on deep insight into how retrieval processes work. The writing is lucid, the conclusions are clear and thoroughly justified, andthe presentation is refreshingly free of prior bias towards one pointof view or another. The paper is also an exemplar of the fact thatan important result does not have to be a demonstration of a novel

technique or of an innovation of some kind. It shows that experimental confirmation (or rebuttal) of previous theories can be a valuable contribution.
A noisy-channel approach to question answering(A. Echihabi and D. Marcu, ACL, 2003)
Commentary: This paper is not well-known in the IR community, but it describes a statistical, language-model approach to question answering can be as effective as more knowledge-based approaches. Given the increasing importance of QA and the overlap with IR, it is critical to show that the statistical approaches ofIR are not superseded by more language and knowledge-based approaches. This paper shows that there is a common basis for these
two tasks and that a statistical framework can be used to capture
and use a lot of linguistic knowledge.
Relevance models in information retrieval(V. Lavrenko and W. B. Croft, In Language Modeling for Information Retrieval, Kluwer Academic Publishers, 2003)
Commentary: In early approaches to applying language modeling in information retrieval, the notion of relevance had not been
explicitly modeled. In particular, it has been difficult to capture
processes such as relevance feedback in the language modelingframework. In this important and ground-breaking paper, the authors develop a formal model which effectively integrates the classical probabilistic model of retrieval with recent developments inestimation techniques, arising from work on language modeling.
There are two main theoretical foundations for the new relevance(language) model (actually, models). One, the classical probabilistic approach, as expressed in the Probability Ranking Principle,
which proposes that documents are ranked according to P (R |Document, Query), where R is the class of relevant documents.
And, two, the various generative language models, which attempt
to estimate P (Query | Document). They propose a basic relevancemodel, and then two distinct approaches based on this model: theprobability ratio approach and the cross-entropy approach. Muchof the theoretical part of the paper is devoted to estimating relevance models, both with and without examples of the set of relevant
documents, and to exploring the role of smoothing in addressing
the problem of high variance in maximum likelihood estimators.
Further, two approaches to estimating probabilities of words in theunknown set of document relevant to a query are presented. In
the experiments, they compare the new relevance model approachagainst the best performing baseline approaches, and demonstratethat the new approach significantly outperforms the already excellent performance of the baselines. The paper provides an excellent
and clear description of the new generative relevance models, supported by a comprehensive set of experiments. Importantly, theauthors provide insightful analysis and argument as to why particular approaches do in fact outperform others. This paper appears in acollection of papers that grew out of a workshop held in May/June
2001 at Carnegie Mellon University. This collection of papers is
recommended to those researchers that intend developing or applying language modeling in IR.
Simple BM25 extension to multiple weighted fields(S. Robertson, H. Zaragoza, and M. Taylor, CIKM, 2004)
Commentary: Due to its simplicity, effectiveness and theoreticalunderpinning, the BM25 measure is now widely used in IR re-search, and a paper describing the measure is an absolute requirement for a volume of IR background reading. However, the SIGIR’94 paper by Robertson and Walker (reproduced in Sparck Jonesand Willett) is slightly out-of-date and should be replaced by amore current paper. Just before the deadline for SWIRL homework,
I received a preprint of the nominated paper, and I realized that it
was an ideal candidate. In addition to presenting an up-to-date version of BM25, it provides valuable insight into how the measureis used in practice, including examples of parameter tuning. The
paper discusses the problem of extending BM25 to structured documents, where terms appearing in certain fields (for example, titles
and anchors) must be given a greater weight. The simplicity of thesolution should be an inspiration to any new researcher.
Interactive cross-language document selection(D. W. Oard, J. Gonzalo, M. Sanderson, F. López-Ostenero, andJ. Wang, Information Retrieval, 2004)
Commentary: It is hard to point to a single seminal work in CLIR
because the innovations were introduced sequentially. The first paper in the modern evolution of the community was a 1990 conference paper by the Bellcore group on cross-language LSI. Introduction of CLIR in TREC in 1996 led to a stream of innovations that depended on the availability of a large test collection, including (1) cross-language blind relevance feedback, introduced byBallesteros and Croft in 1997, (2) structured queries, introduced by
Pirkola in 1998 (building on earlier work by Hull), (3) bidirectionaltranslation, introduced by McCarley in 1999, and the use of translation probabilities trained on parallel corpora, introduced separatelyby three TREC teams (BBN, TNO, and UMass) in 2000. Of these,
the effective use of translation probabilities was the ultimate key
to success – effective use of translation probabilities has a greater
beneficial effect than any other single issue in CLIR. So if I wereto recommend two “must-read” papers in CLIR, I would choose
the Ballesteros and Croft SIGIR 1998 paper (one year after theirfirst one, and thus better developed and also incorporating structured queries) and one of the three initial parallel corpus papers
from TREC-9 (or, in every case, later published journal articles).
Of the three, the BBN paper was the most accessible, but it had
the unusual feature that it adopted an HMM rather than a language
model as a point of departure (mathematically, this choice led tothe same result, though). The TNO and UMass papers were castin the language modeling framework that has come to dominate resent research in IR, so one of them might be a better choice for a“must read” volume where there are sure to be other language modeling papers that will set the reader up to understand that framework
well. Finally, it is important to note that the basic structure of the
IR problem breaks down in cases when the searcher cannot read the
document’s language. This has been the focus of the CLEF interactive track, the most interesting result of which is that current MT
technology is good enough to support interactive selection of documents, and led to the paper recommended here. Another possible
choice with far less detail and more recent results using a QA task
(which proved to be quite interesting) would be the CLEF-2004
interactive track overview paper.
Other comments
Commentary: Other topics that I considered included: machinelearning especially for text categorization, clustering, and information extraction; retrieval from structured data (the web is a special
case of this); models for IR including language models and dimen-sion reduction (LSI/PLSI/topics); personal information management; use of redundancy for QA or information extraction (KnowItAll); and analysis of novelty (a la MMR). And, for fun, I also
looked at the 100 most cited papers in CiteSeer and scanned for IRrelated ones, http://citeseer.ist.psu.edu/source.html. I
realize that this is biased by the nature of the papers that they index (very few HCI/NLP papers, for example), by the age of papers,
etc. Yet, three IR-related papers appeared in the Top100. This list
only includes documents in the CiteSeer.IST database. Citations
where one or more authors of the citing and cited articles match
are not included. The data is automatically generated and may contain errors. The list is generated in batch mode and citation counts
may differ from those currently in the CiteSeer.IST database, because the database is continuously updated. At rank 53 was “Indexing by Latent Semantic Analysis”, Deerwester, Dumais, Furnas
et al. (1990); at rank 63, “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, [Brin and Page, 1998]; and at rank 77,“Authoritative Sources in a Hyperlinked Environment”, Kleinberg(1997).
Commentary: I gave serious consideration to three other papers:“Matching Words and Pictures”, Barnard et al., J. Machine Learning Research, 2003, a seminal paper on automatic keyword indexing of images, but rather rambling, strays beyond IR and has somemethodological problems; “The Automatic Derivation of IR encodements for machine-readable text”, by H. P. Luhn, from Readings in IR, always worth another read, unique and stimulating brief
early work in some ways more relevant in the world of the semantic web than years ago; and “On relevance. . . ”, Maron and Kuhns,
again in Readings, another astonishing piece of work for its day
showing real insight into problems, which in some cases only really impinged on the practical search world with search engines.
Commentary: There are, of course, several other seminal papers
that without question should be included in our consideration that
address other topics. Most notable among those are the original
Brin and Page paper on PageRank, and something from the pioneering work on blind relevance feedback (the UMass LCA paper
comes to mind, but I suspect that there is something that predatesit), and something on statistical significance testing (perhaps Hull’swell received SIGIR paper). One of Hersh’s two SIGIR papers thatwas motivated by the results of the TREC interactive track would
also be an excellent choice, and would something on QA. Thinking even more broadly, the impact of TREC on research in IR has
been so fundamental that a “must read” volume without a TREC
overview would clearly be incomplete. Voorhees [2000] might be
the right one to pick for that.
Acknowledgments
SWIRL 2004 was funded by the Australian Academy of Technological Sciences and Engineering (http://www.atse.org.au/).
We gratefully acknowledge the support of Frontiers of Science andTechnology Mission and Workshop component of the Innovation
Access Program. part of the Australian Government’s Innovation
Statement, Backing Australia’s Ability. We also thank the CSIROICT Centre, RMIT University, and the University of Melbourne for
additional financial support.

Nominated papers
S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman. Basic
local alignment search tool. J. of Molecular Biology, 215:403–
410, 1990.
A. Berger and J. D. Lafferty. Information retrieval as statistical translation. In Proc. Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 222–
229, Berkeley, CA, August 1999. ACM Press, NY. URL http://citeseer.ist.psu.edu/berger99information.html.
K. Bharat and M. Henzinger. Improved algorithms for topic
distillation in a hyperlinked environment. In W. B. Croft,
A. Moffat, C. J. van Rijsbergen, R. Wilkinson, and J. Zobel, editors, Proc. Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pages
104–111, Melbourne, Australia, August 1998. ACM Press,NY.
URL http://gatekeeper.dec.com/pub/DEC/SRC/
publications/monika/sigir98.pdf.
S. Brin and L. Page. The anatomy of a large-scale hypertextual web
search engine. In P. Thistlewaite and H. Ashman, editors, Proc.
7th World Wide Web Conf. (WWW7), pages 107–117, Brisbane,
Australia, April 1998. URL http://decweb.ethz.ch/WWW7/
1921/com1921.htm.
A. Broder. A taxonomy of Web search. SIGIR Forum, 36(2), Fall
2002. URL http://sigir.org/forum/F2002/broder.pdf.
C. Buckley and E. Voorhees. Evaluating evaluation measure stability. In Emmanuel Yannakoudakis, Nicholas J. Belkin, Mun Kew
Leong, and Peter Ingwersen, editors, Proc. Annual Int. ACM
SIGIR Conf. on Research and Development in Information Retrieval, pages 33–40, Athens, Greece, September 2000. ACM
Press, NY. URL http://doi.acm.org/10.1145/345508.
345543.
J. Callan. Distributed information retrieval. In W. Bruce Croft,editor, Advances in Information Retrieval, chapter 5, pages 127–
150. Kluwer Academic Publishers, 2000. URL http://www-2.
cs.cmu.edu/~callan/Papers/ciir00.ps.gz.

M. Hearst. User interfaces and visualization. In R. BaezaYates and B. Ribeiro-Neto, editors, Modern Information Retrieval, pages 257–323. Addison-Wesley Longman, 1999.
URL http://www.sims.berkeley.edu/~hearst/irbook/chapters/chap10.html.
D. G. Hendry and D. J. Harper. An informal information-seeking
environment. J. of the American Society for Information Science,
48(11):1036–1048, 1997.
W. Hersh, A. Turpin, S. Price, D. Kraemer, B. Chan, L. Sacherek,
and D. Olson. Do batch and user evaluations give the same results? In Emmanuel Yannakoudakis, Nicholas J. Belkin, Mun
Kew Leong, and Peter Ingwersen, editors, Proc. Annual Int.
ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 17–24, Athens, Greece, September 2000.
ACM Press, NY. URL http://medir.ohsu.edu/~hersh/sigir-00-batcheval.pdf.
W. R. Hersh, M. K. Crabtree, D. H. Hickam, L. Sacherek,
C. P. Friedman, P. Tidmarsh, C. Moesback, and D. Kraemer. Factors associated with success for searching MEDLINE and applying evidence to answer clinical questions. J. of
the American Medical Informatics Association, 9(3):283–293,
May/June 2002. URL http://medir.ohsu.edu/~hersh/jamia-02-irfactors.pdf.
J. R. Hobbs, D. Appelt, J. Bear, D. Israel, M. Kameyama, M. E.
Stickel, and M. Tyson. FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In
E. Roche and Y. Schabes, editors, Finite-State Language Processing, pages 383–406. MIT Press, 1996. URL http://
citeseer.nj.nec.com/hobbs96fastus.html.
P. Ingwersen. Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. J. of Documentation,
52(1):3–50, 1996.
J. M. Kleinberg. Authoritative sources in a hyperlinked environment. J. of the ACM, 46(5):604–632, 1999.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and
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