Language Modeling for Information Retrieval

  • W. Bruce Croft
  • John Lafferty

Part of the The Springer International Series on Information Retrieval book series (INRE, volume 13)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Victor Lavrenko, W. Bruce Croft
    Pages 11-56
  3. Karen Sparck Jones, Stephen Robertson, Djoerd Hiemstra, Hugo Zaragoza
    Pages 57-71
  4. Wessel Kraaij, Martijn Spitters
    Pages 95-123
  5. William J. Teahan, David J. Harper
    Pages 141-165
  6. Vibhu O. Mittal, Michael J. Witbrock
    Pages 219-244
  7. Back Matter
    Pages 245-245

About this book

Introduction

A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into predefined cat­ egories. The first statisticallanguage modeler was Claude Shannon. In exploring the application of his newly founded theory of information to human language, Shannon considered language as a statistical source, and measured how weH simple n-gram models predicted or, equivalently, compressed natural text. To do this, he estimated the entropy of English through experiments with human subjects, and also estimated the cross-entropy of the n-gram models on natural 1 text. The ability of language models to be quantitatively evaluated in tbis way is one of their important virtues. Of course, estimating the true entropy of language is an elusive goal, aiming at many moving targets, since language is so varied and evolves so quickly. Yet fifty years after Shannon's study, language models remain, by all measures, far from the Shannon entropy liInit in terms of their predictive power. However, tbis has not kept them from being useful for a variety of text processing tasks, and moreover can be viewed as encouragement that there is still great room for improvement in statisticallanguage modeling.

Keywords

DOM Performance Text cognition database filtering machine translation speech recognition

Editors and affiliations

  • W. Bruce Croft
    • 1
  • John Lafferty
    • 2
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstUSA
  2. 2.Computer Science DepartmentCarniege Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-017-0171-6
  • Copyright Information Springer Science+Business Media B.V. 2003
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-6263-5
  • Online ISBN 978-94-017-0171-6
  • Series Print ISSN 1387-5264
  • About this book