Method of word sense disambiguation based on bayes and machine readable dictionary
一种基于贝叶斯分类与机读词典的多义词排歧方法

multl-senses of a word are widespread phenomenon in the natural language. The accuracy rate of sense ambiguation is the most important target of a software on the fields of machine translation, information indexing and text sorting. 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. The experimental results show that it has a high accuracy rate of word sense ambiguation when the scale of markup corpus has been limited.