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A simple rule-based part of speech tagger

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Eric BrillAuthors Info & Claims

ANLC '92: Proceedings of the third conference on Applied natural language processing

Pages 152 - 155

https://doi.org/10.3115/974499.974526

Published: 31 March 1992 Publication History

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Abstract

Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, corpus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.

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Published In

ANLC '92: Proceedings of the third conference on Applied natural language processing

March 1992

273 pages

Program Chairs:
Madeleine Bates
BBN Systems and Technologies
,
Oliviero Stock
IRST, Istituto per la Ricerca Scientifica e Tecnologica

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Association for Computational Linguistics

United States

Publication History

Published: 31 March 1992

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Abstract

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