Natural Language Information Retrieval

1. Front Matter

   Pages i-xxv

   PDF

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

    PDF

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.

• DOM
• Syntax
• classification
• communication
• computational linguistics
• corpus
• corpus linguistic
• information system
• language
• linguistics
• morphology
• natural language

• General Electric, Research & Development, Schenectady, USA

  Tomek Strzalkowski

Policies and ethics