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
收藏
页码：1491 / 1498
页数：7
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