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<Paper uid="W05-0407">
  <Title>Engineering of Syntactic Features for Shallow Semantic Parsing</Title>
  <Section position="6" start_page="53" end_page="54" type="evalu">
    <SectionTitle>
6 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 reranking. It was experimented with the Voted Perceptron and was shown to improve the syntactic parsing. A refinement of such technique was presented in (Taskar et al., 2004). The substructures produced by the proposed tree kernel were bound to local properties of the target parse tree and more lexical information was added to the overall kernel function.</Paragraph>
    <Paragraph position="2"> In (Zelenko et al., 2003), two kernels over syntactic shallow parser structures were devised for the extraction of linguistic relations, e.g. personaffiliation. To measure the similarity between two nodes, the contiguous string kernel and the sparse string kernel (Lodhi et al., 2000) were used. The former can be reduced to the contiguous substring kernel whereas the latter can be transformed in the non-contiguous string kernel. The high running time complexity, caused by the general form of the fragments, limited the experiments on data-set of just 200 news items.</Paragraph>
    <Paragraph position="3"> In (Cumby and Roth, 2003), it is proposed a description language that models feature descriptors to generate different feature type. The descriptors, which are quantified logical prepositions, are instantiated by means of a concept graph which encodes the structural data. In the case of relation extraction the concept graph is associated with a syntactic shallow parse and the extracted propositional features express fragments of a such syntactic structure. The experiments over the named entity class categorization show that when the description language selects an adequate set of tree fragments the Voted Perceptron algorithm increases its classification accuracy. In (Culotta and Sorensen, 2004) a dependency  tree kernel is used to detect the Named Entity classes in natural language texts. The major novelty was the combination of the contiguous and sparse kernels with the word kernel. The results show that the contiguous outperforms the sparse kernel and the bag-of-words.</Paragraph>
  </Section>
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