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<Paper uid="W06-2608">
  <Title>Syntagmatic Kernels: a Word Sense Disambiguation Case Study</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we present a family of kernel functions, named Syntagmatic Kernels, which can be used to model syntagmatic relations. Syntagmatic relations hold among words that are typically collocated in a sequential order, and thus they can be acquired by analyzing word sequences. In particular, Syntagmatic Kernels are defined by applying a Word Sequence Kernel to the local contexts of the words to be analyzed. In addition, this approach allows us to define a semi supervised learning schema where external lexical knowledge is plugged into the supervised learning process. Lexical knowledge is acquired from both unlabeled data and hand-made lexical resources, such as WordNet. We evaluated the syntagmatic kernel on two standard Word Sense Disambiguation tasks (i.e. English and Italian lexical-sample tasks of Senseval-3), where the syntagmatic information plays a crucial role. We compared the Syntagmatic Kernel with the standard approach, showing promising improvements in performance. null</Paragraph>
  </Section>
class="xml-element"></Paper>