Natural Language Information Retrieval

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

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Karen Sparck Jones
    Pages 1-24
  3. Christian Jacquemin, Evelyne Tzoukermann
    Pages 25-74
  4. Tomek Strzalkowski, Fang Lin, Jin Wang, Jose Perez-Carballo
    Pages 113-145
  5. Jussi Karlgren
    Pages 147-166
  6. Yorick Wilks, Robert Gaizauskas
    Pages 197-214
  7. Paul Thompson, Christopher C. Dozier
    Pages 261-272
  8. Louise Guthrie, Joe Guthrie, James Leistensnider
    Pages 289-310
  9. Back Matter
    Pages 375-385

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  • Tomek Strzalkowski
    • 1
  1. 1.General Electric, Research & DevelopmentSchenectadyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-017-2388-6
  • Copyright Information Springer Science+Business Media B.V. 1999
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-5209-4
  • Online ISBN 978-94-017-2388-6
  • Series Print ISSN 1386-291X
  • About this book