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<Paper uid="W06-2909">
  <Title>Semantic Role Labeling via Tree Kernel Joint Inference</Title>
  <Section position="7" start_page="67" end_page="67" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> Recent work on Semantic Role Labeling has shown that to achieve high labeling accuracy a joint inference on the whole predicate argument structure should be applied. As feature design for such task is complex, we can take advantage from kernel methods to model our intuitive knowledge about the n-ary predicate argument relations.</Paragraph>
    <Paragraph position="1"> In this paper we have shown that we can exploit the properties of tree kernels to engineer syntactic features for the semantic role labeling task. The experiments suggest that (1) the information related to the whole predicate argument structure is important as it can improve the state-of-the-art and (2) tree kernels can be used in a joint model to generate relevant syntactic/semantic features. The real drawback is the computational complexity of working with SVMs, thus the design of fast algorithm is an interesting future work.</Paragraph>
  </Section>
class="xml-element"></Paper>