摘要
词义消歧在自然语言处理的许多应用领域都起着十分重要的作用。为了适用于大规模的词义消歧,提出了一种无导的学习方法。基于向量空间模型,结合机读词典和义类词典建立从义项到义类的映射关系,再利用义类知识在语料库中无导学习消歧特征,最后利用这些特征实现词义消歧。
Word Sense Disambiguation(WSD) plays an important role in many areas of natural language processing. In order to deal with large scale WSD, an unsupervised WSD method is provided in this paper. Based on the vector space model, the mapping relationship between word sense and sense categories is set up by using Machine Readable Dictionary (MRD) and sense categories thesaurus; and then use the knowledge of sense categories to learn disambiguate feature in corpus unsupervi ̄sedly; finally, the disambiguate feature is used to disambiguate ambiguous words.
出处
《计算机应用研究》
CSCD
北大核心
2005年第4期39-41,共3页
Application Research of Computers
基金
国家自然科学基金项目 ( 10071028 )
国家语言文字应用委员会语言文字应用"十五"科研项目
关键词
自然语言处理
词义消歧
无导方法
义类
Natural Language Processing
Word Sense Disambiguation
Unsupervised Method
Sense Categories