Word sense disambiguation based on maximum entropy classifier

被引：0

作者：

Zhang C. ^{[1
]}

Zhou X. ^{[2
]}

Gao X. ^{[2
]}

Yu B. ^{[1
]}

机构：

[1] School of Software and Microelectronics, Harbin University of Science and Technology, Harbin

[2] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin

来源：

International Journal of Performability Engineering | 2019年 / 15卷 / 05期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Discriminative features; Maximum entropy classifier; Natural language processing; Semantic category; Word sense disambiguation;

D O I：

10.23940/ijpe.19.05.p26.14911498

中图分类号：

学科分类号：

摘要：

Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing. In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features. At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories. Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model. A test corpus is applied to test the performance of the WSD classifier. Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used. © 2019 Totem Publisher, Inc. All rights reserved.

引用

页码：1491 / 1498

页数：7

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