【摘要】 词义消歧一直是自然语言处理领域的关键问题和难点之一。目前进行的很多词义消歧研究多采用几个多义词作为实验测试对象,在实际应用方面存在着局限性。本文对大规模真实文本进行了词义消歧研究,采用了基于依存分析改进贝叶斯分类模型的有指导词义消歧方法。该模型充分利用依存句法分析,从句子的内部结构,寻找词语之间支配与被支配的关系,借以确定能够对词语语义构成内在限制的上下文,有效地克服了单纯贝叶斯分类器中无关上下文造成的噪声影响。本实验的开放测试正确率可以达到91.89％,封闭实验正确率可达99.4％,验证了改进模型的有效性。更多还原

【Abstract】 Word sense disambiguation has always being a key problem and one of the difficult points in natural language processing. Presently, only some ambiguous words are selected as disambiguated objects in many word sense disambiguation researches, which have great limitations in real application. In this paper, large-scale real texts are researched applying supervised word sense disambiguation approach based on dependency relation analysis and Bayes classifier. By employing completely dependency relationship analysis, this model tries to find dominant and dominated relation among words from intrinsic structure of a sentence to make sure the contexts which limit the word sense and overcome effectively the disturbing results of noise produced by irrespective contexts in mere Bayes classifier. The accuracy of the experiment is 91.89% in open test and 99.4% in close test, substantiating the wonderful performance of dependency relationship analysis and Bayes Model.更多还原