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<Paper uid="W06-2909">
  <Title>Semantic Role Labeling via Tree Kernel Joint Inference</Title>
  <Section position="6" start_page="66" end_page="67" type="relat">
    <SectionTitle>
5 Related Work
</SectionTitle>
    <Paragraph position="0"> Recently, many kernels for natural language applications have been designed. In what follows, we highlight their difference and properties.</Paragraph>
    <Paragraph position="1"> The tree kernel used in this article was proposed in (Collins and Duffy, 2002) for syntactic parsing re-ranking. It was experimented with the Voted Perceptron and was shown to improve the syntactic parsing. In (Cumby and Roth, 2003), a feature description language was used to extract structural features from the syntactic shallow parse trees associated with named entities. The experiments on the named entity categorization showed that when the description language selects an adequate set of tree fragments the Voted Perceptron algorithm increases its classification accuracy. The explanation was that the complete tree fragment set contains many irrelevant features and may cause overfitting. In (Punyakanok et al., 2005), a set of different syntactic parse trees, e.g. the n best trees generated by the Charniak's parser, were used to improve the SRL accuracy. These different sources of syntactic information were used to generate a set of different SRL  outputs. A joint inference stage was applied to resolve the inconsistency of the different outputs. In (Toutanova et al., 2005), it was observed that there are strong dependencies among the labels of the semantic argument nodes of a verb. Thus, to approach the problem, a re-ranking method of role sequences labeled by a TRC is applied. In (Pradhan et al., 2005b), some experiments were conducted on SRL systems trained using different syntactic views.</Paragraph>
  </Section>
class="xml-element"></Paper>