A Simple Rule-Based Part of Speech Tagger

A Simple Rule-Based Part of Speech Tagger Eric Brill * Department of Computer Science University of Pennsylvania Philadelphia, Pennsylvania 19104 U.S.A. brill@unagi.cis.upenn.edu 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 sim- ple rule-based part of speech tagger which au- tomatically acquires its rules and tags with ac- curacy 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, cor- pus genre or language to another. Perhaps the biggest contribution of this work is in demon- strating 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, search- ing for a better and more expressive set of rule templates and other variations on the simple but effective theme described below. 1 Introduction There has been a dramatic increase in the application of probabilistic models to natural language processing over the last few years. The appeal of stochastic techniques over traditional rule-based techniques comes from the ease with which the necessary statistics can be automat- ically acquired and the fact that very little handcrafted knowledge need be built into the system. In contrast, the rules in rule-based systems are usually difficult to construct and are typically not very robust. One area in which the statistical approach has done particularly well is automatic part of speech tagging, as- signing each word in an input sentence its proper part of speech [Church 88; Cutting et al. 92; DeRose 88; Deroualt and Merialdo 86; Garside et al. 87; Jelinek 85; *The author would like to thank Mitch Marcus and Rich Pito for valuable input. This work was supported by DARPA and AFOSR jointly under grant No. AFOSR-90-0066, and by ARO grant No. DAAL 03-89-C0031 PRI. Kupiec 89; Meteer et al. 91]. Stochastic taggers have ob- tained a high degree of accuracy without performing any syntactic analysis on the input. These stochastic part of speech taggers make use of a Markov model which cap- tures lexical and contextual information. The parame- ters of the model can be estimated from tagged ([Church 88; DeRose 88; Deroualt and Merialdo 86; Garside et al. 87; Meteer et al. 91]) or untagged ([Cutting et al. 92; Jelinek 85; Kupiec 89J) text. Once the parameters of the model are estimated, a sentence can then be automat- ically tagged by assigning it the tag sequence which is assigned the highest probability by the model. Perfor- mance is often enhanced with the aid of various higher level pre- and postprocessing procedures or by manually tuning the model. A number of rule-based taggers have been …

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