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This paper addresses the problem of classifying Chinese unknown words into fine-grained semantic categories defined in a Chinese thesaurus, Cilin (Mei et al. 1984). We present three novel knowledge-based models that capture the relationship between the semantic categories of an unknown word and those of its component characters in three different ways, and combine two of them with a corpus-based model that uses contextual information to classify unknown words. Experiments show that the combined knowledge-based model outperforms previous methods on the same task, but the use of contextual information does not further improve performance.