Abstract: To increase the word disambiguation accuracy, a word disambiguation solution based on improved Vector Space Model (VSM) was presented. Since the algorithm takes account of grammar, morphology and semantic and calculates the context similarity requiring the character vector abstraction, the algorithm is able to achieve better results by using collocation constraint. The open test precision can reach 80%. The result shows that the method can fully describe the features of context, and is beneficial to further semantic parsing.

Key words: Vector Space Model, Word disambiguation, Context Similarity

摘要： 为了提高词义排歧的准确率,提出了一种基于改进的向量空间模型(VSM)的词义排歧策略,该模型在提取特征向量的基础上,考虑了语法、词形、语义等因素,计算语境相似度,并引入搭配约束,改进了算法的效果,在开放测试环境下,词义标注正确率可达到80%以上。实验结果表明,该方法对语境信息的描述更加全面,有利于进一步的语义分析。

关键词: 向量空间模型, 词义排歧, 语境相似度