Maximum Entropy-Based Chinese Word Sense Disambiguation
基于最大熵原理的汉语词义消歧

Word sense disambiguation is a crucial problem to be solved in NLP. A supervised machine learning method is proposed in this paper, which is applied in word disambiguation in Chinese. The method combines various features in context to disambiguate word senses. The features include annotations of words, parts of speech and sub- jects etc. And a uniform representation formalizes the features. In this way, the problem of synthesis among various features and knowledge representation of feature will be solved. 20 Chinese polysemous words are tested in our exper- iment. The result with average precision 87% shows that the method is effective.