ADVANCES IN INFORMATION RETRIEVAL. Recent Research from the Center for Intelligent Information Retrieval

Transcription

1 ADVANCES IN INFORMATION RETRIEVAL Recent Research from the Center for Intelligent Information Retrieval

2 THE KLUWER INTERNATIONAL SERIES ON INFORMATION RETRIEVAL Series Editor W. Bruce Croft University of Massachusetts, Amherst Also in the Series: MULTIMEDIA INFORMATION RETRIEVAL: Content-Based Information Retrieval from Large Text and Audio Databases,.. by Peter Schauble; ISBN: INFORMATION RETRIEVAL SYSTEMS, by Gerald Kowalski; ISBN: CROSS-LANGUAGE INFORMATION RETRIEVAL, edited by Gregory Grefenstette; ISBN: X TEXT RETRIEVAL AND FILTERING: Analytic Models of Performance, by Robert M. Losee; ISBN: INFORMATION RETRIEVAL: UNCERTAINTY AND LOGICS: Advanced Models for the Representation and Retrieval of Information, by Fabio Crestani, Mounia Lalmas, and Cornelis Joost van Rijsbergen; ISBN: DOCUMENTCOMPUTING: Technologies for Managing Electronic Document Collections, by Ross Wilkinson, Timothy Arnold-Moore, Michael Fuller, Ron Sacks-Davis, James Thom, and Justin Zobel; ISBN: AUTOMATIC INDEXING AND ABSTRACTING OF DOCUMENT TEXTS, by Marie-Francine Moens; ISBN

3 ADVANCES IN INFORMATION RETRIEVAL Recent Research from the Center for Intelligent Information Retrieval Edited by W. Bruce Croft University of Massachusetts, Amherst KLUWER ACADEMIC PUBLISHERS New York / Boston / Dordrecht / London / Moscow

4 ebook ISBN: Print ISBN: Kluwer Academic Publishers New York, Boston, Dordrecht, London, Moscow Print 2000 Kluwer Academic Publishers Massachusetts All rights reserved No part of this ebook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher Created in the United States of America Visit Kluwer Online at: and Kluwer's ebookstore at:

5 Contents Preface ix Contributing Authors xiii 1 Combining Approaches to Information Retrieval 1 W. Bruce Croft 1 2 Introduction Combining Representations 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 Information Retrieval Research 37 Warren R. Greiff 1 Introduction 37 2 Exploratory Data Analysis 39 3 Weight of Evidence 40 4 Analysis of the Relationship between Document Frequency and the Weight of Evidence of Term Occurrence 43 5 Probabilistic Modeling of Multiple Sources of Evidence 53 6 Conclusions 70 3 Language Models for Relevance Feedback 73 Jay M. Ponte 1 Introduction 73 2 The Language Modeling Approach to IR 75 3 Related Work 78 4 Query Expansion in the Language Modeling Approach 83

6 vi ADVANCES IN INFORMATION RETRIEVAL 5 Discussion and Future Work 92 4 Topic Detection and Tracking: Event Clustering as a Basis for First Story 97 Detection Ron Papka James Allan 1 Topic Detection and Tracking 98 2 On-lineClustering Algorithms Experimental Setting Event Clustering First Story Detection Discussion of First Story Detection Conclusion Future Work Distributed Information Retrieval 127 Jamie Callan 1 Introduction Multi-Database Testbeds Resource Description Resource Selection Merging Document Rankings Acquiring Resource Descriptions Summary and Conclusions Topic-Based Language Models for Distributed Retrieval Jinxi Xu W. Bruce Croft 1 2 Introduction Topic Models K-Means Clustering Four Methods of Distributed Retrieval Experimental Setup Global Clustering Recall-basedRetrieval Distributed Retrieval in Dynamic Environments More Clusters Better Choice of Initial Clusters Local Clustering Multiple-Topic Representation Efficiency RelatedWork Conclusion and FutureWork The Effect of Collection Organization and Query Locality on Information 173 Retrieval System Performance Zhihong Lu Kathryn S. McKinley 1 Introduction Related Work

7 Contents 3 SystemArchitectures Configuration with Respect to Collection Organization, Collection Access Skew, and Query Locality SimulationModel Experiments Conclusions Cross-LanguageRetrieval via TransitiveTranslation Lisa A. Ballesteros Introduction Translation Resources Dictionary Translation and Ambiguity Resolving Ambiguity Addressing Limited Resources Summary Building, Testing, and Applying ConceptHierarchies 235 Mark Sanderson Dawn Lawrie 1 Introduction Building a Concept Hierarchy Presenting a Concept Hierarchy Evaluating the Structures Future Work Conclusions 261 Appendix: ANOVA analysis Appearance-Based Global Similarity Retrieval of Images S. Ravela C. Luo 1 Introduction Appearance Related Representations Computing Global Appearance Similarity TrademarkRetrieval Conclusions and Limitations 299 Index 305 vii 267

8 Preface The Center for Intelligent Information Retrieval (CIIR) was formed in the Computer Science Department of the University of Massachusetts, Amherst in The core support for the Center came from a National Science Foundation State/Industry/University Cooperative Research Center (S/IUCRC) grant, although there had been a sizeable information retrieval (IR) research group for over 10 years prior to that grant. The basic goal of these Centers is to combine 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 retrieval 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

9 x ADVANCES IN INFORMATION RETRIEVAL language models contributes to these results. Greiff s paper, The Use of Exploratory Data Analysis in Information Retrieval Research, introduces a datadriven approach to developing retrieval models and uses this approach to 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 Basis 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 information retrieval. In Distributed Information Retrieval, Callan gives an overview of this area of research and summarizes the results related to database description 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 intermediate language. In Building, Testing and Applying Concept Hierarchies, Sanderson and Lawrie describe research in the important new area of summarization. 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.

10 PREFACE xi These papers, like the research in the CIIR, cover a wide variety of topics in the general area of IR. Together, they represent a snapshot of the state-ofthe-art in information retrieval at the turn of the century and at the end of a decade that has seen the advent of the World-Wide Web. The papers have been written to provide overviews of their subareas and to serve as source material for graduate and undergraduate courses in information retrieval. Finally, I would liketo acknowledge thefaculty, staff, and students associated with the CIIR since 1992 who have contributed enormously to its success. In particular, Jean Joyce, Kate Moruzzi, and Glenn Stowell havebeen instrumental tothecenter s operation. I wouldalso liketothankwin Aung, ournsfprogram Manager, for his support over the years. BRUCE CROFT

11 Contributing Authors James Allan is an Assistant Professor in the Computer Science Department and Assistant Director of the CIIR at the University of Massachusetts, Amherst. He received the Ph.D. in Computer Science from Cornell University in His research interests are in information retrieval and organization, as well as topic detection and tracking. Lisa Ballesteros is an Assistant Professor in the Computer Science Department at Mount Holyoke College, Massachusetts. She received the B.S. degree in Biology from Union College, Schenectady, N.Y. in 1987 and the M.S. degree in Computer Science from the University of Massachusetts, Amherst in She is currently a Ph.D. candidate in Computer Science at U.Mass. Her major research in terest is cross-language retrieval. Jamie Callan received a Ph.D. in Computer Science from the University of Massachusetts, Amherst, in He was a Research Assistant Professor at U.Mass. from 1994 to 1999, and was Assistant Director of the CIIR from 1995 to He has been an Associate Professor at Carnegie Mellon University since the Fall of Dr. Callan s research interests include software architectures for IR, distributed information retrieval, information filtering, and use of the Internet and IR tools for K-12 education. W. Bruce Croft is a Professor of Computer Science and Director of the CIIR at the University of Massachusetts, Amherst. He holds a Ph.D. from Cambridge University, England and has been active in information retrieval research since 1975.

12 xiv ADVANCES IN INFORMATION RETRIEVAL Warren Greiff received his Bachelor s Degree in Mathematics from Case Institute of Technology and Master s Degrees from the University of Pennsylvania and Antioch College in Computer Science and Education, respectively. After a number of years working in American industry, he spent 12 years as a professor at the University of the Americas in Puebla, Mexico. In 1994, he returned to the United States to pursue a doctoral degree, which he received from the University of Massachusetts, Amherst in He is currently pursuing his research interests in statistical techniques applied to natural language processing technology at the Mitre Corporation in Bedford, Massachusetts. Dawn Lawrie is a Computer Science graduate student at the University of Massachusetts, Amherst. She received her bachelor s degree in Computer Science from Dartmouth College in Currently she is studying the organization of information and text mining at the CIIR. Zhihong Lu received her Ph.D. in Computer Science from the University of Massachusetts, Amherst, and is currently working in AT&T network services. Her dissertation work is on scalable distributed architectures for information retrieval. Chen Luo is a graduate student at the University of Massachusetts, Amherst. He received his B.S. from the Automation Department of Tsinghua University, China. His research interests are image retrieval, pattern recognition and computer vision. Kathryn S. McKinley is an Associate Professor at the University of Massachusetts. She received her Ph.D. from Rice University in Her research interests include compilers, architectures, parallel and distributed computing with a particular focus on memory hierarchy and program analysis, and information retrieval. Ron Papka is currently the Director of Research and Development at Dataware Technologies. He is also an Adjunct Professor of Computer Science at Smith College. He received his Ph.D. in Computer Science from the University of Massachusetts in 1999, an M.S. from Brown University in 1993, and B.S. from Columbia University in the same discipline. His research interests include Information Retrieval, Machine Learning, and Object-Oriented Programming. Jay M. Ponte received his B.S. in computer science from Northeastern University in He received his M.S. and Ph.D. degrees in Computer Science

13 Contributing Authors xv from the University of Massachusetts in 1996 and 1998 respectively, where he worked in the CIIR. He is currently a Principal Member of Technical Staff at GTE Laboratories where he continues to specialize in information retrieval and statistical natural language processing. S. Chandu Ravela is a doctoral candidate at the University of Massachusetts, Amherst. Mr. Ravela s research interests are visual media representations, computer vision, scale-space and multi-sensor data abstractions. Mr. Ravela received his M.S.C.S in 1994 and B.E. in Computer Science and Engineering from the Regional Engineering College, Trichy, India in 1991 and M.S. in Computer Science from the U.Mass. in Mark Sanderson is a lecturer in the Information Science department at the University of Sheffield. Prior to this, he was a postdoctoral researcher in the CIIR at the University of Massachusetts. He completed his Ph.D. on disambiguation and IR at the University of Glasgow in 1997 and works on summarization, hypertext retrieval, and spoken document retrieval. Jinxi Xu received his Ph.D. in Computer Science from the University of Massachusetts, Amherst in 1997, and was a post-doctoral research associate for two years at the CIIR. He is now a scientist at BBN Technologies in Cambridge, Massachusetts. His research interests include information retrieval and natural language processing.