Chinese Word Sense Disambiguation using a LSTM

Abstract

Word sense disambiguation (WSD) is a challenging natural language processing (NLP) problem. We propose a new strategy for WSD, which at first replaces the interesting word in a sentence by the different synonyms corresponding to the different meanings, and then justify whether the transformed sentence is “legal”. A legal sentence is still legal after one or more word are replaced by other ones with the same meaning. A long short-term memory (LSTM) network-based model is proposed to perform the sentence/text classification. Furthermore, we build a Chinese WSD dataset based on HIT-CIR Tongyici Cilin (Extended) dataset. The model is evaluated on the new dataset and achieves better performance than the state-of-the-art.