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<Paper uid="W02-1116">
  <Title>A Maximum Entropy Approach to HowNet-Based Chinese Word Sense Disambiguation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a maximum entropy method for the disambiguation of word senses as defined in HowNet. With the release of this bilingual (Chinese and English) knowledge base in 1999, a corpus of 30,000 words was sense tagged and released in January 2002. Concepts meanings in HowNet are constructed by a closed set of sememes, the smallest meaning units, which can be treated as semantic tags. The maximum entropy model treats semantic tags like parts-of-speech tags and achieves an overall accuracy of 89.39%, outperforming a baseline system, which picks the most frequent sense.</Paragraph>
  </Section>
class="xml-element"></Paper>