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<Paper uid="W06-2608">
  <Title>Syntagmatic Kernels: a Word Sense Disambiguation Case Study</Title>
  <Section position="8" start_page="61" end_page="62" type="concl">
    <SectionTitle>
7 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> In this paper we presented the Syntagmatic Kernels, i.e. a set of kernel functions that can be used to model syntagmatic relations for a wide variety of Natural Language Processing tasks. In addition, we proposed twosoft-matching criteria forthesequence analysis, which can be easily modeled by relaxing the constraints in a Gap-Weighted Subsequences Kernel applied to local contexts of the word to be analyzed. Experiments, performed on two lexical sample Word Sense Disambiguation benchmarks, show that our approach further improves the standard techniques usually adopted to deal with syntagmatic relations. In addition, the Domain Proximity soft-matching criterion allows us to define a semi-supervised learning schema, improving the overall results.</Paragraph>
    <Paragraph position="1"> For the future, we plan to exploit the Syntagmatic Kernel for a wide variety of Natural Language Processing tasks, such as Entity Recognition and Relation Extraction. In addition we are applying the soft matching criteria here defined to Tree Kernels,  in order to take into account lexical variability in parse trees. Finally, we are going to further improve the soft-matching criteria here proposed by exploring the use of entailment criteria for substitutability.</Paragraph>
  </Section>
class="xml-element"></Paper>