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<Paper uid="W04-0825">
  <Title>Contextual Semantics for WSD</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In the framework of the Senseval-3 evaluation campaign on Word Sense Disambiguation (WSD), we presented two systems relying on different strategy. The system SynLexEn is an evolution from the system used during the Senseval-2 campaign. It is based on two steps.</Paragraph>
    <Paragraph position="1"> The first step uses semantic classification trees on a short context size. A decision system based on document similarity is used as second step.</Paragraph>
    <Paragraph position="2"> The novelty of this system resides in a new vision level on the context. The semantic dictionary of Sinequa is extensively used in this process.</Paragraph>
    <Paragraph position="3"> The second system, SynLexEn2, is based on weighted clues summation over a short context size. From the training data, a score is computed for each word in a short context size, for each sense.</Paragraph>
    <Paragraph position="4"> In Section 2, the combined approach system for WSD is presented. We first give an overview of the data pre-processing that was applied (Section 2.1). Then, a brief description of Semantic Classification Trees is given (Section 2.2) along with a description of additional data used for semantic view of short and long context (Section 2.3 and Section 2.4). Next, a semantic information retrieval system used in order to select the appropriate sense is proposed (Section 2.5).</Paragraph>
    <Paragraph position="5"> Finally, the SynLexEn2 system is presented in Section 3. We then conclude with the evaluation results for both systems in Section 4.</Paragraph>
  </Section>
class="xml-element"></Paper>