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<Paper uid="C96-2209">
  <Title>A tagger/lemmatiser for Dutch medical language</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Medical patient reports consist mainly of free text, combined with results of various laboratories. While nmnerical data can easily be stored and processed for archiving and research purposes, free text is rather difficult to be processed by a computer, although it contains the most relevant information. IIowever, only a few NLP-driven systems have actually been implemented (lfi'iedman and Johnson, 1992).</Paragraph>
    <Paragraph position="1"> For Dutch, a prototype covering a larger part of the Dutch grammar and medical vocabulary is under development.</Paragraph>
    <Paragraph position="2"> This paper focuses on a spin-off-- c.q. a contextual tagger/lemmatiser (T/L) -of the lexical component of the Dutch Medical Language Processor (DMLP) (Spyns and De Moor, 1996). A T/L is quite valuable for several kinds of corpus studies concerning the medical vocabulary (co-occurrence patterns, statistical data, . .. ). For efficient sentence analysis in particular, it is necessary to disambiguate the results of morphological analysis before they can be passed oil the parser.</Paragraph>
    <Paragraph position="3"> In the following sections, we will describe in detail the different knowledge bases (cf. section 2) and the implementation of tile major data structures (cf. section 3). Each section is illustrated by an cxaInple or some implementation details. The subsequent section (4) is devoted to the evaluation. The paper ends with a discussion (section 5).</Paragraph>
  </Section>
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