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.
为解决该问题，提出一种基于文本分类的视觉单词歧义性分析方法。
This paper proposes a visual words ambiguity analysis method based on text categorization. The codebook is generated by the BOW model.
同时，查询分类可以作为降低跨语言检索系统查询翻译的歧义性的技术手段。
Moreover, query classification could be the technical method to reduce the query translation disambiguation of CLIR.
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