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<Paper uid="I05-3012">
  <Title>Integrating Collocation Features in Chinese Word Sense Disambiguation</Title>
  <Section position="6" start_page="92" end_page="93" type="concl">
    <SectionTitle>
5 Conclusion and the Future Work
</SectionTitle>
    <Paragraph position="0"> This paper reports a corpus-based Word Sense Disambiguation approach for Chinese word using local collocation features and topical contextual features. Compared with the base-line systems in which a Naive Bayes classifier is constructed by combining the contextual features with the bi-gram features, the new system achieves 3% precision improvement in average in Peoples' Daily News corpus and 10% improvement in SENSEVAL-3 data set. Actually, it works very well when disambiguating the sense with sparse distribution over the entire corpus under which the statistic calculation prone to identify it incorrectly. In the same time, because disambiguating using collocation fea- null tures does not need statistical calculation, it makes contribution to reduce the size of human tagged corpus needed which is critical and time consuming in corpus based approach.</Paragraph>
    <Paragraph position="1"> Because different types of collocations may play different roles in classifying the sense of an ambiguous word, we hope to extend this work by integrating collocations with different weight based on their types in the future, which may need a pre-processing job to categorize the collocations automatically.</Paragraph>
  </Section>
class="xml-element"></Paper>