Word Sense Disambiguation Using Context Translation

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原文链接

NETL
Springer Nature

外文摘要：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。

外文关键词：

Data sparsenessContext translationBayesian modelTranslationParameter estimation

作者：

Zhizhuo Yang、Hu Zhang、Qian Chen、Hongye Tan

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作者单位：

School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China

会议名称：

International conference on computer processing of oriental languages;CCF conference on natural language processing and Chinese computing

会议地点：

Kunming(CN)

会议母体文献：

Natural language understanding and intelligent applications

页码：

489-496

出版时间：

2016

DOI：

10.1007/978-3-319-50496-4_41