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<Paper uid="C02-1040">
  <Title>Learning Verb Argument Structure from Minimally Annotated Corpora</Title>
  <Section position="6" start_page="3" end_page="3" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we discussed a technique which automatically identified the correct argument structure of a set of verbs. Our results in this paper serve as a replication and extension of the results in (Merlo and Stevenson, 2001). Our main contribution in this paper is to show that with reasonable accuracy, this task can be accomplished using only tagged and chunked data. In addition, we incorporate some additional features such as part-of-speech tags and the use of subcategorization frame learning as part of our classification algorithm.</Paragraph>
    <Paragraph position="1"> We exploited the distributions of selected features from the local context of the verb which was extracted from a 23M word WSJ corpus. We used C5.0 to construct a decision tree classifier using the values of those features. We were able to construct a classifier that has an error rate of 33.4%. This work shows that a subcategorization frame learning algorithm (Sarkar and Zeman, 2000) can be applied to the task of classifying verbs into verb alternation classes.</Paragraph>
    <Paragraph position="2"> In future work, we would like to classify verbs into alternation classes on a per-token basis (as is done in the approach taken by Gildea (2002)) rather than the per-type we currently employ and also incorporate information about word senses in order to feasibly include verb alternation information in a statistical parser.</Paragraph>
  </Section>
class="xml-element"></Paper>