Coarse-Grained Word Sense Disambiguation Using Features Described in the Lexicon
Tao Guo · Zhongwen xinxi xuebao · 2007
This paper presents a simple but effective feature-based approach to Chinese word sense disambiguation using the distributional features available from the Grammatical Knowledge-base of Contemporary Chinese. The test data is the sense-tagged corpus of People's Daily.A Nave Bayes classifier is also tried as a comparable statistical method.The feature-based approach achieves precision of 90%,which is comparable to the NB classifier.The striking advantages of the feature-based approach are 1) It is not influenced by the data size,and 2) It can disambiguate some specific words with precision of 100%.The features appropriate for different parts of speech in Chinese WSD are also discussed.This paper demonstrates that sense features described in the lexicon are worth including in WSD.