Hybrid Models for Semantic Classification of Chinese Unknown Words

This paper addresses the problem of classifying Chinese unknown words into fine-grained semantic categories defined in a Chinese thesaurus. We describe 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. We then combine two of the knowledge-based models with a corpus-based model which classifies unknown words using contextual information. Experiments show that the knowledge-based models outperform previous methods on the same task, but the use of contextual information does not further improve performance.