实验结果表明语义范畴的引入有助于提高算法的学习效率和词义排歧的正确率。
Experimental results show that the semantic categorization knowledge is useful for improving the learning efficiency of the algorithm and accuracy of disambiguation.
提出了一种基于贝叶斯分类与机读词典的多义词排歧方法，通过小规模语料库的训练和歧义词在机读词典中的语义定义来完成歧义的消除。
A method based on the bayes and machine readable dictionary was proposed, which could disambiguate by the training of a small-scale corpus and the definition of semantic in machine dictionary.
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