Word sense disambiguation (WSD) plays an important role in many areas of natural language processing, such as machine translation, information retrieval, sentence analysis, and speech recognition. Research on WSD has great theoretical and practical significance. The main purposes of this study were to study the kind of knowledge that is useful for WSD, and to establish a new WSD model based on syntagmatic features, which can be used to disambiguate noun sense in Mandarin Chinese effectively. Close correlation has been found between lexical meaning and its distribution.
According to a study in the field of cognitive science [Choueka, 1983], people often disambiguate word sense using only a few other words in a given context (frequently only one additional word). Thus, the relationships between one word and others can be effectively used to resolve ambiguity. Based on a descriptive study of more than 4,000 Chinese noun senses, a multi-level framework of syntagmatic analysis was designed to describe the syntactic and semantic constraints of Chinese nouns. All of these polyseme nouns were surveyed, and it was found that different senses have different and complementary distributions at the syntax and/or collocation levels. This served as a foundation for establishing an WSD model by using grammatical information and a thesaurus provided by linguists.The model uses the Grammatical Knowledge-base of Contemporary Chinese [Yu Shiwen et al. 2002] as one of its main machine-readable dictionaries (MRDs). It can provide rich grammatical information for disambiguation of Chinese lexicons, such as parts-of-speech (POS) and syntax functions. Another resource of the model is the Semantic Dictionary of Contemporary Chinese [Wang Hui et al. 1998], which provides a thesaurus and semantic collocation information of more than 20,000 nouns. They were employed to analyze 635 Chinese polysemous nouns.
By making full use of these two MRD resources and a very large POS-tagged corpus of Mandarin Chinese, a multi-level WSD model based on syntagmatic features was developed. The experiment described at the end of the paper verifies that the approach achieves high levels of efficiency and precision.