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Natural Language Information Retrieval

  • Book
  • © 1999

Overview

Editors:
  1. Tomek Strzalkowski

    1. General Electric, Research & Development, Schenectady, USA

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Part of the book series: Text, Speech and Language Technology (TLTB, volume 7)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxv
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  2. What is the Role of NLP in Text Retrieval?

    • Karen Sparck Jones
    Pages 1-24

  3. NLP for Term Variant Extraction: Synergy Between Morphology, Lexicon, and Syntax

    • Christian Jacquemin, Evelyne Tzoukermann
    Pages 25-74

  4. Combining Corpus Linguistics and Human Memory Models for Automatic Term Association

    • Gerda Ruge
    Pages 75-98

  5. Using NLP or NLP Resources for Information Retrieval Tasks

    • Alan F. Smeaton
    Pages 99-111

  6. Evaluating Natural Language Processing Techniques in Information Retrieval

    • Tomek Strzalkowski, Fang Lin, Jin Wang, Jose Perez-Carballo
    Pages 113-145

  7. Stylistic Experiments in Information Retrieval

    • Jussi Karlgren
    Pages 147-166

  8. Extraction-Based Text Categorization: Generating Domain-Specific Role Relationships Automatically

    • Ellen Riloff, Jeffrey Lorenzen
    Pages 167-196

  9. LaSIE Jumps the GATE

    • Yorick Wilks, Robert Gaizauskas
    Pages 197-214

  10. Phrasal Terms in Real-World IR Applications

    • Joe Zhou
    Pages 215-259

  11. Name Recognition and Retrieval Performance

    • Paul Thompson, Christopher C. Dozier
    Pages 261-272

  12. COLLAGE: An NLP Toolset to Support Boolean Retrieval

    • Jim Cowie
    Pages 273-287

  13. Document Classification and Routing

    • Louise Guthrie, Joe Guthrie, James Leistensnider
    Pages 289-310

  14. Murax: Finding and Organizing Answers from Text Search

    • Julian M. Kupiec
    Pages 311-332

  15. The Use of Categories and Clusters for Organizing Retrieval Results

    • Marti A. Hearst
    Pages 333-374

  16. Back Matter

    Pages 375-385
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Keywords

About this book

The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa­ tion access has exploded. Emerging applications in computer-assisted infor­ mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the­ art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In­ formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.

Editors and Affiliations

  • General Electric, Research & Development, Schenectady, USA

    Tomek Strzalkowski

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