Table Of ContentADVANCES IN
INFORMATION RETRIEVAL
Recent Research from the
Center for Intelligent Information Retrieval
THE KLUWER INTERNATIONAL SERIES
ON INFORMATION RETRIEVAL
Series Editor
W.Bruce Croft
University ofMassachusetts, Amherst
AlsointheSeries:
MULTIMEDIA INFORMATION RETRIEVAL: Content-Based
Information Retrieval from LargeText and Audio Databases,
by Peter Sch..auble; ISBN: 0-7923-9899-8
INFORMATIONRETRIEVALSYSTEMS,byGeraldKowalski;ISBN:
0-7923-9926-9
CROSS-LANGUAGEINFORMATIONRETRIEVAL,editedbyGregory
Grefenstette; ISBN: 0-7923-8 122-X
TEXT RETRIEVAL ANDFILTERING: AnalyticModels of
Performance, by Robert M. Losee; ISBN: 0-7923-8177-7
INFORMATIONRETRIEVAL: UNCERTAINTYANDLOGICS:
Advanced Models for the Representation and Retrieval of
Information,by Fabio Crestani, Mounia Lalmas, and Cornelis
Joost van Rijsbergen; ISBN: 0-7923-8302-8
DOCUMENTCOMPUTING: Technologies for ManagingElectronic
Document Collections, by Ross Wilkinson, Timothy Arnold-Moore,
Michael Fuller, Ron Sacks-Davis, James Thom, and Justin Zobel;
ISBN: 0-7923-8357-5
AUTOMATICINDEXINGANDABSTRACTINGOFDOCUMENT
TEXTS, by Marie-Francine Moens; ISBN 0-7923-7793-1
ADVANCES IN
INFORMATION RETRIEVAL
Recent Research from the
Center for Intelligent Information Retrieval
Edited by
W. Bruce Croft
Universityof Massachusetts, Amherst
KLUWER ACADEMIC PUBLISHERS
NewYork / Boston/Dordrecht/London / Moscow
eBook ISBN: 0-306-47019-5
Print ISBN: 0-792-37812-1
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Print ©2000 Kluwer Academic Publishers
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Contents
Preface ix
ContributingAuthors xiii
1
Combining Approaches to Information Retrieval 1
W. Bruce Croft
1 Introduction 1
2 Combining Representations 5
3 Combining Queries 9
4 Combining Ranking Algorithms 11
5 Combining Search Systems 13
6 Combining Belief 16
7 Language Models 25
8 Conclusion 28
2
The Use of Exploratory Data Analysis in 37
InformationRetrievalResearch
WarrenR. Greiff
1 Introduction 37
2 ExploratoryDataAnalysis 39
3 Weight ofEvidence 40
4 AnalysisoftheRelationshipbetweenDocumentFrequencyandthe
43
Weight of Evidenceof Term Occurrence
5 Probabilistic Modeling of Multiple Sources of Evidence 53
6 Conclusions 70
3
LanguageModels forRelevance Feedback 73
Jay M. Ponte
1 Introduction 73
2 TheLanguageModelingApproachtoIR 75
3 RelatedWork 78
4 QueryExpansionintheLanguageModelingApproach 83
vi ADVANCES IN INFORMATION RETRIEVAL
5 DiscussionandFutureWork 92
4
Topic Detection and Tracking: EventClustering as aBasis forFirst Story 97
Detection
Ron PapkaJamesAllan
1 TopicDetectionandTracking 98
2 On-lineClusteringAlgorithms 103
3 ExperimentalSetting 108
4 EventClustering 110
5 First Story Detection 112
6 Discussion of First Story Detection 119
7 Conclusion 120
8 Future Work 122
5
Distributed Information Retrieval 127
Jamie Callan
127
1 Introduction
2 Multi-Database Testbeds 129
3 Resource Description 130
4 Resource Selection 131
5 Merging Document Rankings 135
6 Acquiring Resource Descriptions 137
7 Summary and Conclusions 145
6
Topic-Based Language Models for Distributed Retrieval
151
Jinxi Xu W. Bruce Croft
1 Introduction 152
2 Topic Models 154
3 K-Means Clustering 155
4 Four Methods of Distributed Retrieval 155
5 ExperimentalSetup 158
6 Global Clustering 160
7 Recall-basedRetrieval 163
8 Distributed Retrieval in Dynamic Environments 165
9 MoreClusters 165
10 Better Choice of Initial Clusters 165
11 Local Clustering 166
12 Multiple-Topic Representation 166
13 Efficiency 168
14 RelatedWork 168
15 Conclusion andFutureWork 169
7
The Effect of Collection Organization and Query Locality on Information 173
RetrievalSystemPerformance
Zhihong Lu Kathryn S. McKinley
1 Introduction 174
2 Related Work 176
Contents vii
3 SystemArchitectures 181
4 ConfigurationwithRespecttoCollectionOrganization,Collection
AccessSkew,andQueryLocality 185
5 SimulationModel 188
6 Experiments 189
7 Conclusions 197
8
Cross-LanguageRetrievalviaTransitiveTranslation 203
LisaA. Ballesteros
1 Introduction 203
2 TranslationResources 205
3 DictionaryTranslationandAmbiguity 208
4 ResolvingAmbiguity 209
5 AddressingLimitedResources 212
6 Summary 230
9
Building,Testing,andApplyingConceptHierarchies 235
MarkSandersonDawn Lawrie
1 Introduction 235
2 Building a Concept Hierarchy 238
3 Presenting a Concept Hierarchy 246
4 Evaluating the Structures 251
5 FutureWork 255
6 Conclusions 261
Appendix: ANOVA analysis 262
10
Appearance-Based Global Similarity Retrieval of Images 267
S. Ravela C. Luo
1 Introduction 268
2 Appearance Related Representations 272
3 Computing Global Appearance Similarity 278
4 TrademarkRetrieval 293
5 Conclusions andLimitations 299
Index 305
Preface
The Center for Intelligent Information Retrieval (CIIR) was formed in the
ComputerScienceDepartmentoftheUniversityofMassachusetts,Amherstin
1992. The core support for the Center came from a National Science Founda-
tionState/Industry/UniversityCooperativeResearchCenter(S/IUCRC)grant,
although there had been a sizeable information retrieval (IR) research group for
over10yearspriortothatgrant. ThebasicgoaloftheseCentersistocombine
basic research, applied research, and technology transfer. The CIIR has been
successful in each of these areas, in that it has produced over 270 research papers,
has been involved in many successful government and industry collaborations,
and has had a significant role in high-visibility Internet sites and start-ups. As
a result of these efforts, the CIIR has become known internationally as one of
the leading research groups in the area of information retrieval.
The CIIR focuses on research that results in more effective and efficient
access and discovery in large, heterogeneous, distributed, text and multimedia
databases. The scope of the work that is done in the CIIR is broad and goes
significantly beyond “traditional” areas of information retrieval such as retrieval
models, cross-lingual search, and automatic query expansion. The research
includes both low-level systems issues such as the design of protocols and
architectures for distributed search, as well as more human-centered topics such
as user interface design, visualization and data mining with text, and multimedia
retrieval.
The papers in this book contain some of the more recent research results
from the CIIR. The first group of papers present new research related to the re-
trieval models that underly IR systems. The first paper, Combining Approaches
to Information Retrieval by Croft, discusses retrieval models and strategies for
combining evidence from multiple document representations, queries, ranking
algorithms and search systems. This has been an important line of research
for more than 10 years, and this paper provides a framework for understanding
the many experimental results in this area and indicates how recent work on
x ADVANCES IN INFORMATION RETRIEVAL
language models contributes to these results. Greiff’s paper, The Use of Ex-
ploratory Data Analysis in Information Retrieval Research, introduces a data-
drivenapproachtodeveloping retrievalmodelsand usesthisapproachto derive
a probabilistic ranking formula from an analysis of TREC data. A number of
retrieval experiments are used to validate this new model. In the third paper
of this group, Language Models for Relevance Feedback, Ponte describes the
language modeling approach to IR that he introduced in his thesis work, and
then shows how this approach can be used for relevance feedback and filtering
environments. A number of experiments demonstrate the effectiveness of this
conceptually simple, but potentially very powerful retrieval model.
The next paper, Topic Detection and Tracking: Event Clustering as a Ba-
sis for First Story Detection by Allan and Papka, describes a relatively new
area of research that focuses on detecting significant events in broadcast news.
New algorithms and modifications of existing IR techniques are presented and
evaluated in the context of this novel task.
The next three papers deal with a range of topics related to distributed infor-
mation retrieval. In Distributed Information Retrieval, Callan gives an overview
of this area of research and summarizes the results related to database de-
scription and selection, and merging rankings from multiple systems. Xu and
Croft, in their paper Topic-Based Language Models for Distributed Retrieval,
present recent results from an approach to describing databases that is based
on identifying language models through clustering. Lu and McKinley discuss
performance-related issues in their paper The Effect of Collection Organization
and Query Locality on Information Retrieval System Performance. They show
that the use of database replication combined with selection algorithms can
significantly improve the efficiency and scalability of distributed retrieval.
The next paper, Cross-Language Retrieval via Transitive Translation by
Ballesteros, discusses the language resources and techniques that are used for
IR in multiple languages. The paper describes a series of experiments using a
dictionary-based approach to transitive translation and retrieval through an in-
termediate language. In Building, Testing and Applying Concept Hierarchies,
Sanderson and Lawrie describe research in the important new area of summa-
rization. They focus specifically on a technique for constructing a hierarchy
of concepts to summarize the contents of a group of documents, such as those
retrieved by a query.
The last paper, Appearance-Based Global Similarity Retrieval of Images
by Ravela and Luo, presents new research in the important, emerging area of
image retrieval. Because many of the techniques used for image indexing and
comparison are very different to those used for text retrieval, the paper contains
an extensive introduction to those techniques. Retrieval evaluations of a new
indexing technique based on image “appearance” are described and discussed.
PREFACE xi
Thesepapers, liketheresearch in theCIIR, coverawide variety oftopics
inthegeneral areaofIR.Together, theyrepresentasnapshotofthe“state-of-
the-art” in information retrieval attheturn ofthecentury andattheendofa
decadethathasseentheadventoftheWorld-WideWeb. Thepapershavebeen
writtentoprovideoverviewsoftheirsubareas andtoserveassourcematerial
forgraduateandundergraduatecoursesininformationretrieval.
Finally,Iwouldliketoacknowledgethefaculty,staff,andstudentsassociated
withtheCIIR since 1992whohavecontributedenormouslytoitssuccess. In
particular,JeanJoyce,KateMoruzzi,andGlennStowellhavebeeninstrumental
totheCenter’soperation. IwouldalsoliketothankWinAung,ourNSFProgram
Manager,forhissupportovertheyears.
BRUCE CROFT
