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<Paper uid="P06-1044">
  <Title>Automatic Classification of Verbs in Biomedical Texts</Title>
  <Section position="3" start_page="0" end_page="345" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Lexical classes which capture the close relation between the syntax and semantics of verbs have attracted considerable interest in NLP (Jackendoff, 1990; Levin, 1993; Dorr, 1997; Prescher et al., 2000). Such classes are useful for their ability to capture generalizations about a range of linguistic properties. For example, verbs which share the meaning of 'manner of motion' (such as travel, run, walk), behave similarly also in terms of subcategorization (I traveled/ran/walked, I traveled/ran/walked to London, I traveled/ran/walked five miles). Although the correspondence between the syntax and semantics of words is not perfect and the classes do not provide means for full semantic inferencing, their predictive power is nevertheless considerable.</Paragraph>
    <Paragraph position="1"> NLP systems can benefit from lexical classes in many ways. Such classes define the mapping from surface realization of arguments to predicate-argument structure, and are therefore an important component of any system which needs the latter. As the classes can capture higher level abstractions they can be used as a means to abstract away from individual words when required.</Paragraph>
    <Paragraph position="2"> They are also helpful in many operational contexts where lexical information must be acquired from small application-specific corpora. Their predictive power can help compensate for lack of data fully exemplifying the behavior of relevant words.</Paragraph>
    <Paragraph position="3"> Lexical verb classes have been used to support various (multilingual) tasks, such as computational lexicography, language generation, machine translation, word sense disambiguation, semantic role labeling, and subcategorization acquisition (Dorr, 1997; Prescher et al., 2000; Korhonen, 2002). However, large-scale exploitation of the classes in real-world or domain-sensitive tasks has not been possible because the existing classifications, e.g. (Levin, 1993), are incomprehensive and unsuitable for specific domains.</Paragraph>
    <Paragraph position="4"> While manual classification of large numbers of words has proved difficult and time-consuming, recent research shows that it is possible to automatically induce lexical classes from corpus data with promising accuracy (Merlo and Stevenson, 2001; Brew and Schulte im Walde, 2002; Korhonen et al., 2003). A number of ML methods have been applied to classify words using features pertaining to mainly syntactic structure (e.g. statistical distributions of subcategorization frames (SCFs) or general patterns of syntactic behaviour, e.g. transitivity, passivisability) which have been extracted from corpora using e.g. part-of-speech tagging or robust statistical parsing techniques.</Paragraph>
    <Paragraph position="5">  This research has been encouraging but it has so far concentrated on general language. Domain-specific lexical classification remains unexplored, although it is arguably important: existing classifications are unsuitable for domain-specific applications and these often challenging applications might benefit from improved performance by utilizing lexical classes the most.</Paragraph>
    <Paragraph position="6"> In this paper, we extend an existing approach to lexical classification (Korhonen et al., 2003) and apply it (without any domain specific tuning) to the domain of biomedicine. We focus on biomedicine for several reasons: (i) NLP is critically needed to assist the processing, mining and extraction of knowledge from the rapidly growing literature in this area, (ii) the domain lexical resources (e.g. UMLS metathesaurus and lexicon1) do not provide sufficient information about verbs and (iii) being linguistically challenging, the domain provides a good test case for examining the potential of automatic classification.</Paragraph>
    <Paragraph position="7"> We report an experiment where a classification is induced for 192 relatively frequent verbs from a corpus of 2230 biomedical journal articles.</Paragraph>
    <Paragraph position="8"> The results, evaluated with domain experts, show that the approach is capable of acquiring classes with accuracy higher than that reported in previous work on general language. We discuss reasons for this and show that the resulting classes differ substantially from those in extant lexical resources.</Paragraph>
    <Paragraph position="9"> They constitute the first syntactic-semantic verb classification for the biomedical domain and could be readily applied to support BIO-NLP.</Paragraph>
    <Paragraph position="10"> We discuss the domain-specific issues related to our task in section 2. The approach to automatic classification is presented in section 3. Details of the experimental evaluation are supplied in section 4. Section 5 provides discussion and section 6 concludes with directions for future work.</Paragraph>
  </Section>
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