First of all, the significance of unsupervised word sense disambiguation study is introduced.
首先，介绍了无监督词义消歧研究的意义。
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。
In the context of the study, it involved giving the volunteers groups of sentences and asking them to work out whether each line made sense -and to remember the last word of each sentence.
研究人员在实验中给了受试者一组句子，要求他们指出每行句子是否有意义，并记住每句的最后一个单词。
The results of this study indicate that the SKCC is effective for word sense disambiguation in MT system and are likely to be important for general Chinese NLP.
初步的实验结果表明，该方法可以有效地进行汉语名词、动词、形容词的词义消歧。
The results of this study indicate that the SKCC is effective for word sense disambiguation in MT system and are likely to be important for general Chinese NLP.
初步的实验结果表明，该方法可以有效地进行汉语名词、动词、形容词的词义消歧。
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