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<Paper uid="C04-1087">
  <Title>Enhancing automatic term recognition through recognition of variation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Terminological processing has long been recognised as one of the crucial aspects of systematic knowledge acquisition and of many NLP applications (IR, IE, corpus querying, etc.). However, term variation has been under-discussed and is rarely accounted for in such applications.</Paragraph>
    <Paragraph position="1"> When naming a new concept, scientists and specialists usually follow some predefined term formation patterns, a process which does not exclude the usage of term variations or alternative names for concepts. Term variations are very frequent: approximately one third of term occurrences are variants (Jacquemin, 2001). They occur not only in text, but also in controlled, manually curated terminological resources (e.g.</Paragraph>
    <Paragraph position="2"> UMLS (NLM, 2004)).</Paragraph>
    <Paragraph position="3"> The task of an automatic term recognition (ATR) system is not only to suggest the most likely candidate terms from text, but also to correlate them with synonymous term variants. In this paper, we briefly present an analysis of term variation phenomena, whose results are subsequently incorporated into a corpus-based ATR method in order to enhance its performance.</Paragraph>
    <Paragraph position="4"> The paper is organised as follows. In Section 2, we analyse the main types of term variation, and briefly examine how existing ATR systems treat them. Our approach to incorporating variants into ATR is presented in Section 3. In Section 4, we evaluate our approach by comparing it to a baseline method (the method without variation recognition), and we conclude the paper in Section 5.</Paragraph>
  </Section>
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