The Word Sense Disambiguation (WSD) study based on large scale real world corpus is performed using an unsupervised learning algorithm based on DGA improved Bayesian Model.

采用基于依存分析改进贝叶斯网络的无指导的机器学习方法对汉语大规模真实文本进行词义消歧实验。

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Word sense disambiguation (WSD) plays an important role in many areas of natural language processing such as machine translation, information retrieval, sentence analysis, speech recognition.

词义排歧在机器翻译、信息检索、句子分析和语音识别等许多领域有重要的作用。

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Word Sense Disambiguation (WSD) has always been a difficult and hot points in natural language processing.

词义消歧是自然然语言处理中的一个难点和热点问题。

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