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Abstract

An introduction to the principal generic approaches to information retrieval research with their associated concepts, models and systems, this text is designed to keep the information professional up to date with the major themes and developments that have preoccupied researchers in recent month in relation to textual and documentary retrieval systems.

COMPASS

Information retrieval

Content

First published 1991 as New horizons in information retrieval

Date

26. 7.2002 20:22:46

Footnote

Rez. in: Managing information 3(1996) no.10, S.49 (D. Bawden); Program 32(1998) no.2, S.190-192 (C. Revie)

LCSH

Information retrieval

Subject

Information retrieval
Information retrieval

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Abstract

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing.

Footnote

Rez. in: JASIST 58(2007) no.6, S.910-911 (J.L. Vicedo u. J. Gomez): "The Text REtrieval Conference (TREC) is a yearly workshop hosted by the U.S. government's National Institute of Standards and Technology (NIST) that fosters and supports research in information retrieval as well as speeding the transfer of technology between research labs and industry. Since 1992, TREC has provided the infrastructure necessary for large-scale evaluations of different text retrieval methodologies. TREC impact has been very important and its success has been mainly supported by its continuous adaptation to the emerging information retrieval needs. Not in vain, TREC has built evaluation benchmarks for more than 20 different retrieval problems such as Web retrieval, speech retrieval, or question-answering. The large and intense trajectory of annual TREC conferences has resulted in an immense bulk of documents reflecting the different eval uation and research efforts developed. This situation makes it difficult sometimes to observe clearly how research in information retrieval (IR) has evolved over the course of TREC. TREC: Experiment and Evaluation in Information Retrieval succeeds in organizing and condensing all this research into a manageable volume that describes TREC history and summarizes the main lessons learned. The book is organized into three parts. The first part is devoted to the description of TREC's origin and history, the test collections, and the evaluation methodology developed. The second part describes a selection of the major evaluation exercises (tracks), and the third part contains contributions from research groups that had a large and remarkable participation in TREC. Finally, Karen Spark Jones, one of the main promoters of research in IR, closes the book with an epilogue that analyzes the impact of TREC on this research field.
... TREC: Experiment and Evaluation in Information Retrieval is a reliable and comprehensive review of the TREC program and has been adopted by NIST as the official history of TREC (see http://trec.nist.gov). We were favorably surprised by the book. Well structured and written, chapters are self-contained and the existence of references to specialized and more detailed publications is continuous, which makes it easier to expand into the different aspects analyzed in the text. This book succeeds in compiling TREC evolution from its inception in 1992 to 2003 in an adequate and manageable volume. Thanks to the impressive effort performed by the authors and their experience in the field, it can satiate the interests of a great variety of readers. While expert researchers in the IR field and IR-related industrial companies can use it as a reference manual, it seems especially useful for students and non-expert readers willing to approach this research area. Like NIST, we would recommend this reading to anyone who may be interested in textual information retrieval."

LCSH

Information storage and retrieval systems / Congresses

RSWK

Information Retrieval / Textverarbeitung / Aufsatzsammlung (BVB)
Kongress / Information Retrieval / Kongress (GBV)

Subject

Information Retrieval / Textverarbeitung / Aufsatzsammlung (BVB)
Kongress / Information Retrieval / Kongress (GBV)
Information storage and retrieval systems / Congresses

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Content

Enthält die Beiträge: GREFENSTETTE, G.: The Problem of Cross-Language Information Retrieval; DAVIS, M.W.: On the Effective Use of Large Parallel Corpora in Cross-Language Text Retrieval; BALLESTEROS, L. u. W.B. CROFT: Statistical Methods for Cross-Language Information Retrieval; Distributed Cross-Lingual Information Retrieval; Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing; EVANS, D.A. u.a.: Mapping Vocabularies Using Latent Semantics; PICCHI, E. u. C. PETERS: Cross-Language Information Retrieval: A System for Comparable Corpus Querying; YAMABANA, K. u.a.: A Language Conversion Front-End for Cross-Language Information Retrieval; GACHOT, D.A. u.a.: The Systran NLP Browser: An Application of Machine Translation Technology in Cross-Language Information Retrieval; HULL, D.: A Weighted Boolean Model for Cross-Language Text Retrieval; SHERIDAN, P. u.a. Building a Large Multilingual Test Collection from Comparable News Documents; OARD; D.W. u. B.J. DORR: Evaluating Cross-Language Text Filtering Effectiveness

Footnote

Rez. in: Machine translation review: 1999, no.10, S.26-27 (D. Lewis): "Cross Language Information Retrieval (CLIR) addresses the growing need to access large volumes of data across language boundaries. The typical requirement is for the user to input a free form query, usually a brief description of a topic, into a search or retrieval engine which returns a list, in ranked order, of documents or web pages that are relevant to the topic. The search engine matches the terms in the query to indexed terms, usually keywords previously derived from the target documents. Unlike monolingual information retrieval, CLIR requires query terms in one language to be matched to indexed terms in another. Matching can be done by bilingual dictionary lookup, full machine translation, or by applying statistical methods. A query's success is measured in terms of recall (how many potentially relevant target documents are found) and precision (what proportion of documents found are relevant). Issues in CLIR are how to translate query terms into index terms, how to eliminate alternative translations (e.g. to decide that French 'traitement' in a query means 'treatment' and not 'salary'), and how to rank or weight translation alternatives that are retained (e.g. how to order the French terms 'aventure', 'business', 'affaire', and 'liaison' as relevant translations of English 'affair'). Grefenstette provides a lucid and useful overview of the field and the problems. The volume brings together a number of experiments and projects in CLIR. Mark Davies (New Mexico State University) describes Recuerdo, a Spanish retrieval engine which reduces translation ambiguities by scanning indexes for parallel texts; it also uses either a bilingual dictionary or direct equivalents from a parallel corpus in order to compare results for queries on parallel texts. Lisa Ballesteros and Bruce Croft (University of Massachusetts) use a 'local feedback' technique which automatically enhances a query by adding extra terms to it both before and after translation; such terms can be derived from documents known to be relevant to the query.
Christian Fluhr at al (DIST/SMTI, France) outline the EMIR (European Multilingual Information Retrieval) and ESPRIT projects. They found that using SYSTRAN to machine translate queries and to access material from various multilingual databases produced less relevant results than a method referred to as 'multilingual reformulation' (the mechanics of which are only hinted at). An interesting technique is Latent Semantic Indexing (LSI), described by Michael Littman et al (Brown University) and, most clearly, by David Evans et al (Carnegie Mellon University). LSI involves creating matrices of documents and the terms they contain and 'fitting' related documents into a reduced matrix space. This effectively allows queries to be mapped onto a common semantic representation of the documents. Eugenio Picchi and Carol Peters (Pisa) report on a procedure to create links between translation equivalents in an Italian-English parallel corpus. The links are used to construct parallel linguistic contexts in real-time for any term or combination of terms that is being searched for in either language. Their interest is primarily lexicographic but they plan to apply the same procedure to comparable corpora, i.e. to texts which are not translations of each other but which share the same domain. Kiyoshi Yamabana et al (NEC, Japan) address the issue of how to disambiguate between alternative translations of query terms. Their DMAX (double maximise) method looks at co-occurrence frequencies between both source language words and target language words in order to arrive at the most probable translation. The statistical data for the decision are derived, not from the translation texts but independently from monolingual corpora in each language. An interactive user interface allows the user to influence the selection of terms during the matching process. Denis Gachot et al (SYSTRAN) describe the SYSTRAN NLP browser, a prototype tool which collects parsing information derived from a text or corpus previously translated with SYSTRAN. The user enters queries into the browser in either a structured or free form and receives grammatical and lexical information about the source text and/or its translation.

Series

The Kluwer International series on information retrieval

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LCSH

Information storage and retrieval systems / Evaluation

RSWK

Information Retrieval / Datenbankverwaltung / Hochschulschrift (GBV)
Information Retrieval / Dialogsystem (SWB)
Information Retrieval / Dialogsystem / Leistungsbewertung (BVB)

Subject

Information Retrieval / Datenbankverwaltung / Hochschulschrift (GBV)
Information Retrieval / Dialogsystem (SWB)
Information Retrieval / Dialogsystem / Leistungsbewertung (BVB)
Information storage and retrieval systems / Evaluation

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Imprint

Washington : Information resources press

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Abstract

Using subject headings for Online Retrieval is an indispensable tool for online system desingners who are developing new systems or refining exicting ones. The book describes subject analysis and subject searching in online catalogs, including the limitations of retrieval, and demonstrates how such limitations can be overcome through system design and programming. The book describes the Library of Congress Subject headings system and system characteristics, shows how information is stored in machine readable files, and offers examples of and recommendations for successful methods. Tables are included to support these recommendations, and diagrams, graphs, and bar charts are used to provide results of data analyses.

Footnote

Rez. in: Information processing and management 31(1995) no.3, S.450-451 (R.R. Larson); Library resources and technical services 41(1997) no.1, S.60-67 (B.H. Weinberg)