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A simple rule-based part of speech tagger

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Eric BrillAuthors Info & Claims

HLT '91: Proceedings of the workshop on Speech and Natural Language

Pages 112 - 116

https://doi.org/10.3115/1075527.1075553

Published: 23 February 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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HLT '91: Proceedings of the workshop on Speech and Natural Language

February 1992

487 pages

ISBN:1558602720

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Association for Computational Linguistics

United States

Publication History

Published: 23 February 1992

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Zaman FMaqbool OKanwal J(2024)Leveraging Bidirectionl LSTM with CRFs for Pashto TaggingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/364945623:4(1-17)Online publication date: 27-Feb-2024
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Jensen EDale MDonnelly PStone CKelly SGodley AD'Mello SBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Toward Automated Feedback on Teacher Discourse to Enhance Teacher LearningProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376418(1-13)Online publication date: 21-Apr-2020
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