Word Sense Disambiguation using Context Translation

摘要：

　　Word Sense Disambiguation(WSD)is one of the key issues in natural language processing.Currently,supervised WSD methods are effective ways to solve the ambiguity problem.However,due to lacking of large-scale training data,they cannot achieve satisfactory results.In this paper,we present a WSD method based on context translation.The method is based on the assumption that translation under the same context expresses similar meanings.The method treats context words consisting of translation as the pseudo training data,and then derives the meaning of ambiguous words by utilizing the knowledge from both training and pseudo training data.Experimental results show that the proposed method can significantly improve traditional WSD accuracy by 3.17%,and outperformed the best participating system in the SemEval-2007: task #5 evaluation.

关键词： Data sparseness Context translation Bayesian model Translation Parameter estimation

作者: Zhizhuo Yang Hu Zhang Qian Chen Hongye Tan

作者单位: School of Computer & Information Technology,ShanXi University,TaiYuan,ShanXi,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-8

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）