A WordNet-like linguistic resource is useful, but difficult to construct. This article proposes a method to integrate five linguistic resources, including English/Chinese sense-tagged corpora, English/Chinese thesauruses, and a bilingual dictionary. Chinese words are mapped into WordNet. A Chinese WordNet and a Chinese-English WordNet are derived by following the structures of WordNet. Experiments with Chinese-English information retrieval are developed to evaluate the applicability of the Chinese-English WordNet. The best model achieves 0.1010 average precision, 69.23 % of monolingual information retrieval. It also gains a 10.02 % increase relative to a model that resolves translation ambiguity and target polysemy problems together.

This paper presents a character-based model of automatic sense determination for Chinese compounds. The model adopts a sense approximation approach using synonymous compounds retrieved by measuring similarity of semantic template in compounding. The similarity measure is derived from an association network among characters and senses, which is built from a formatted MRD. Adopting the taxonomy of CILIN, a system of deep semantic classification (at least to the small classes) for V-V compounds is implemented and evaluated to test the model. The experiment reports a high precision rate (about 38 % in outside test and 61 % in inside test) against the baseline one (about 18%). 1.

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. 1