Building a Chinese WordNet Via Class-Based Translation Model

Semantic lexicons are indispensable to research in lexical semantics and word sense disambiguation (WSD). For the study of WSD for English text, researchers have been using different kinds of lexicographic resources, including machine readable dictionaries (MRDs), machine readable thesauri, and bilingual corpora. In recent years, WordNet has become the most widely used resource for the study of WSD and lexical semantics in general. This paper describes the Class-Based Translation Model and its application in assigning translations to nominal senses in WordNet in order to build a prototype Chinese WordNet. Experiments and evaluations show that the proposed approach can potentially be adopted to speed up the construction of WordNet for Chinese and other languages.