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<Paper uid="P06-1044">
  <Title>Automatic Classification of Verbs in Biomedical Texts</Title>
  <Section position="8" start_page="350" end_page="351" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has shown that current domain-independent NLP and ML technology can be used to automatically induce a relatively high accuracy verb classification from a linguistically challenging corpus of biomedical texts. The lexical classification resulting from our work is strongly domain-specific (it differs substantially from previous ones) and it can be readily used to aid BIO-NLP. It can provide useful material for investigating the syntax and semantics of verbs in biomedical data or for supplementing existing domain lexical resources with additional information (e.g.</Paragraph>
    <Paragraph position="1"> 13The different sub-domains of the biomedical domain may, of course, be even more conventionalized (Friedman et al., 2002).</Paragraph>
    <Paragraph position="2">  semantic classifications with additional member verbs). Lexical resources enriched with verb class information can, in turn, better benefit practical tasks such as parsing, predicate-argument identification, event extraction, identification of biomedical relation patterns, among others.</Paragraph>
    <Paragraph position="3"> In the future, we plan to improve the accuracy of automatic classification by seeding it with domain-specific information (e.g. using named entity recognition and anaphoric linking techniques similar to those of Vlachos et al. (2006)). We also plan to conduct a bigger experiment with a larger number of verbs and demonstrate the usefulness of the bigger classification for practical BIO-NLP application tasks. In addition, we plan to apply similar technology to other interesting domains (e.g.</Paragraph>
    <Paragraph position="4"> tourism, law, astronomy). This will not only enable us to experiment with cross-domain lexical class variation but also help to determine whether automatic acquisition techniques benefit, in general, from domain-specific tuning.</Paragraph>
  </Section>
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