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Statistical methods in language processing.

Steven Abney1

  • 1Department of Linguistics, University of Michigan, Ann Arbor, MI 48109-1220, USA.

Wiley Interdisciplinary Reviews. Cognitive Science
|August 25, 2015
PubMed
Summary

Statistical methods, using machine learning and large datasets, dominate computational linguistics for language processing. These powerful techniques, however, have seen limited adoption in general linguistics.

Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Information Theory

Background:

  • Statistical methods have been the dominant methodology in computational linguistics since the 1990s.
  • This approach is characterized by stochastic models, extensive data, machine learning, and rigorous evaluation.
  • The widespread adoption in computational linguistics contrasts with limited penetration into general linguistics.

Purpose of the Study:

  • To define and delineate the scope of statistical methods in computational linguistics.
  • To highlight the origins and characteristics of these methods.
  • To discuss their applicability to language processing.

Main Methods:

  • Focus on statistical methodologies prevalent in computational linguistics since 1990.

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  • Exclusion of standard hypothesis testing and experimental design.
  • Emphasis on methods with direct applicability to language processing.

    • Main Results:

    • Statistical methods are integral to nearly all current work in computational linguistics.
    • These methods are primarily borrowed from machine learning and information theory.
    • The techniques have broad applicability beyond linguistics.

    Conclusions:

    • Statistical methods are a cornerstone of modern computational linguistics.
    • Their influence is profound within the field but minimal in general linguistics.
    • The focus remains on practical applications in language processing.