基于统计的汉语词义消歧研究  

Study on Chinese Word Sense Disambiguation Based on Statistics

作　　者：原媛[1] 彭建华[1] 张汝云[1] 

机构地区：[1]信息工程大学国家数字交换系统工程技术研究中心,河南郑州450002

出　　处：《信息工程大学学报》2007年第4期501-504,共4页Journal of Information Engineering University

基　　金：国家863计划资助项目(2007AA01Z434)

摘　　要：词义消歧一直是信息检索领域的关键问题和难点之一。统计学方法以其良好的词义消歧效果逐渐占据主流地位。文章结合义类词典HowNet,分别采用隐马尔可夫模型,贝叶斯模型,基于依存关系改进贝叶斯模型对大规模真实文本进行了词义消歧研究,其中基于依存关系改进贝叶斯模型的词义消歧效果最好。Word sense disambiguation has always been a key problem and one of the difficult points in information retrieval. Statistics have good effect on word sense disambiguation,and gradually takes up the mainstream status. In this paper,large-scale real texts are researched with HowNet, respectively applying Hidden Markov Model, Naive Bayes Modei,Bayes Model and Dependency Parsing, among which Bayes Model and Dependency Parsing have the best effect.

关 键 词：词义消歧 HOWNET 隐马尔可夫模型 贝叶斯模型 依存分析 

分 类 号：O212[理学—概率论与数理统计]