A Method of Word Sense Disambiguation with Recurrent Netural Networks

Abstract

Word sense disambiguation is an important research problem in natural language processing field. For the phenomenon that a Chinese word has many senses, recurrent neural network (RNN) is used to determine true meaning of ambiguous word with its context. Target ambiguous word is viewed as center and its four adjacent word units are extracted. Word, part.of.speech and semantic categories are extracted as disambiguation features. Based on disambiguation features, recurrent neural network is used to construct word sense disambiguation classifier. Training corpus in SemEval.2007: Task5 and semantic annotation corpus in Harbin Institute of Technology are used to optimize parameters of RNN. Test corpus in SemEval.2007: Task#5 is applied to test word sense disambiguation classifier. Experimental results show that the proposed method can improve accuracy of word sense disambiguation.

Keywords

• word sense disambiguation
• natural language processing
• recurrent neural network
• disambiguation features