摘要
本文依据《现代汉语语法信息词典》中对词语多义的属性特征描述,对《人民日报》语料中155个词语共4996个同形实例进行了粗粒度词义自动消歧实验,同时用贝叶斯算法进行了比较测试。基于词典属性特征的消歧方法在同形层面上准确率达到90%,但召回率偏低。其优点在于两个方面:1)不受词义标注语料库规模的影响;2)对特定词语意义的消歧准确率可达到100%。本文也讨论了适用于不同词类的消歧特征。
This paper presents a simple but effective feature-based approach to Chinese word sense disambiguation using the distributional features available from the Grammatical Knowledge-base of Contemporary Chinese. The test data is the sense-tagged corpus of People＇s Daily. A Naive Bayes classifier is also tried as a comparable statistical method. The feature-based approach achieves precision of 90%, which is comparable to the NB classifier. The striking advantages of the feature-based approach are 1） It is not influenced by the data size, and 2） It can disambiguate some specific words with precision of 100%. The features appropriate for different parts of speech in Chinese WSD are also discussed. This paper demonstrates that sense features described in the lexicon are worth including in WSD.
出处
《中文信息学报》
CSCD
北大核心
2007年第2期3-8,共6页
Journal of Chinese Information Processing
基金
国家973计划资助项目(2004CB318102)
关键词
人工智能
自然语言处理
特征
词义
词义消歧
贝叶斯分类法
artificial intelligence
natural language processing~ feature
word sense
word sense disambiguation
Naive Bayes classifier
