基于词典属性特征的粗粒度词义消歧

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 4 996 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。

Coarse-Grained Word Sense Disambiguation Using Features Described in the Lexicon

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 Nave 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.