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<Paper uid="A92-1021">
  <Title>A Simple Rule-Based Part of Speech Tagger</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 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 automatically 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.</Paragraph>
    <Paragraph position="1"> One area in which the statistical approach has done particularly well is automatic part of speech tagging, assigning each word in an input sentence its proper part of speech \[Church 88; Cutting et al. 92; DeRose 88; Deroualt and Merialdo 86; Garside et al. 87; Jelinek 85; *The author would like to thank Mitch Marcus and Rich Pito for valuable input. This work was supported by DARPA and AFOSR jointly under grant No. AFOSR-90-0066, and by ARO grant No. DAAL 03-89-C0031 PRI.</Paragraph>
    <Paragraph position="2"> Kupiec 89; Meteer et al. 91\]. Stochastic taggers have obtained 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 captures lexical and contextual information. The parameters of the model can be estimated from tagged (\[Church 88; DeRose 88; Deroualt and Merialdo 86; Garside et al.</Paragraph>
    <Paragraph position="3"> 87; Meteer et al. 91\]) or untagged (\[Cutting et al. 92; Jelinek 85; Kupiec 89J) text. Once the parameters of the model are estimated, a sentence can then be automatically tagged by assigning it the tag sequence which is assigned the highest probability by the model. Performance is often enhanced with the aid of various higher level pre- and postprocessing procedures or by manually tuning the model.</Paragraph>
    <Paragraph position="4"> A number of rule-based taggers have been built \[Klein and Simmons 63; Green and Rubin 71; Hindle 89\]. \[Klein and Simmons 63\] and \[Green and Rubin 71\] both have error rates substantially higher than state of the art stochastic taggers. \[Hindle 89\] disambiguates words within a deterministic parser. We wanted to determine whether a simple rule-based tagger without any knowledge of syntax can perform as well as a stochastic tagger, or if part of speech tagging really is a domain to which stochastic techniques are better suited.</Paragraph>
    <Paragraph position="5"> In this paper we describe a rule-based tagger which performs as well as taggers based upon probabilistic models. The rule-based tagger overcomes the limitations common in rule-based approaches to language processing: it is robust, and the rules are automatically acquired. In addition, the tagger has many advantages over stochastic taggers, including: a vast reduction in stored information required, the perspicuity of a small set of meaningful rules as opposed to the large tables of statistics needed for stochastic taggers, ease of finding and implementing improvements to the tagger, and better portability from one tag set or corpus genre to another.</Paragraph>
  </Section>
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