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<Paper uid="I05-3008">
  <Title>Word Meaning Inducing via Character Ontology: A Survey on the Semantic Prediction of Chinese Two-Character Words Shu-Kai Hsieh Seminar f&amp;quot;ur Sprachwissenschaft</Title>
  <Section position="6" start_page="61" end_page="62" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we propose a system that aims to gain the possible semantic classes of unknown words via similarity computation based on character ontology and CILIN thesaurus. In general, we approach the task in a hybrid way that combines the strengths of ontology-based and example-based model to achieve at better result for this task.</Paragraph>
    <Paragraph position="1"> The scheme we use for automatic semantic class prediction takes advantage of the presumptions that the conceptual information wired in Chinese characters can help retrieve the near- null synonyms, and the near-synonyms constitute a key indicator for the semantic class guess of unknown words in question.</Paragraph>
    <Paragraph position="2"> The results obtained show that, our SC prediction algorithm can achieve fairly high level of performance. While the work presented here in still in progress, a first attempt to analyze a test set of 800 examples has already shown a 43.60% correctness for VV compounds, 41.00% for VN compounds, and 74.50% for NN compounds at the level-3 of CILIN. If shallow semantics is taken into consideration, the results are even better. Working in this framework, however, one point as suggested by other similar approach is that, human language processing is not limited to an abstract ontology alone (Hong et al. 2004). In practical applications, ontologies are seldom used as the only knowledge resources. For those unknown words with very weak semantic transparency, it would be interesting to show that an ontology-based system can be greatly boosted when other information sources such as metaphor and etymological information integrated. Future work is aimed at improving this accuracy by adding other linguistic knowledge sources and extending the technique to WSD (Word Sense Disambiguation). null</Paragraph>
  </Section>
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