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<Paper uid="W04-2419">
  <Title>Semantic Role Labeling using Maximum Entropy Model</Title>
  <Section position="6" start_page="3" end_page="3" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we propose a semantic role labeling method using a maximum entropy model. Because the maximum entropy model enables not only to exploit rich features but also to alleviate the data sparseness problem, we use it to model the probability of a semantic role label sequence. The proposed method has following characteristics: firstly, it assigns the semantic role labels to the constituents in the immediate clause, and then assigns role labels to the constituents in the upper clauses, and it utilizes the relation between syntactic and semantic characteristics of a given context.</Paragraph>
    <Paragraph position="1"> For the future work, we will device a method of clustering for the path and predicate features, and include the clustering results as additional features.</Paragraph>
  </Section>
class="xml-element"></Paper>