中文摘要：

词义消歧一直是自然语言处理领域中的关键性问题。为了提高词义消歧的准确率,从目标歧义词汇出发,挖掘左右词单元的语义知识。以贝叶斯模型为基础,结合左右词单元的语义信息,提出了一种新的词义消歧方法。以SemEval-2007：Task#5作为训练语料和测试语料,对词义消歧分类器进行优化,并对优化后的分类器进行测试。实验结果表明：词义消歧的准确率有所提高。

英文摘要：

Word sense disambiguation is an important problem in natural language processing. In order to improve the precision of word sense disambiguation, semantic knowledge of left and right word units is mined starting from the target polysemous word. Based on the Bayesian model, a new method of word sense disambiguation is proposed with semantic information of left and right word units. SemEval-2007： Task#5 is used as training corpus and test corpus. The classifier of word sense disambiguation is optimized. Then the optimized classifier is tested. Experimental results show that the precision of word sense disambiguation is improved.