北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (3): 114-119.doi: 10.13190/j.jbupt.2018-148

• 研究报告 • 上一篇    下一篇

基于卷积神经网络的词义消歧

张春祥1, 赵凌云2, 高雪瑶2   

  1. 1. 哈尔滨理工大学 软件与微电子学院, 哈尔滨 150080;
    2. 哈尔滨理工大学 计算机科学与技术学院, 哈尔滨 150080
  • 收稿日期:2018-07-11 出版日期:2019-06-28 发布日期:2019-06-20
  • 通讯作者: 高雪瑶(1979-),女,副教授,硕士生导师,E-mail:xueyao_gao@163.com. E-mail:xueyao_gao@163.com
  • 作者简介:张春祥(1974-),男,教授,硕士生导师.
  • 基金资助:
    国家自然科学基金项目(61502124,60903082);中国博士后科学基金项目(2014M560249);黑龙江省普通高校基本科研业务费专项资金项目(LGYC2018JC014);黑龙江省自然科学基金项目(F2015041,F201420)

Word Sense Disambiguation Based on Convolution Neural Network

ZHANG Chun-xiang1, ZHAO Ling-yun2, GAO Xue-yao2   

  1. 1. School of Software and Microelectronics, Harbin University of Science and Technology, Harbin 150080, China;
    2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2018-07-11 Online:2019-06-28 Published:2019-06-20

摘要: 为了提高词义消歧性能,提出了一种基于卷积神经网络的消歧方法.以歧义词为中心,向左右两侧连续扩展4个邻接词汇单元,选取其中的词形、词性和语义类作为消歧特征.以消歧特征为基础,使用卷积神经网络来确定歧义词的语义类别.利用SemEval-2007:Task#5的训练语料和哈尔滨工业大学语义标注语料来优化卷积神经网络.使用SemEval-2007:Task#5的测试语料来测试词义消歧分类器的性能,所提方法的消歧平均准确率有提高.实验结果表明,该方法在词义消歧中是可行的.

关键词: 词义消歧, 卷积神经网络, 消歧特征, 语义类别

Abstract: In order to improve the performance of word sense disambiguation (WSD), a disambiguation method based on convolution neural network (CNN) is proposed. Ambiguous word is viewed as center and four adjacent word units around its left and right sides are extended. Word, part-of-speech and semantic categories are extracted as disambiguation features. Based on disambiguation features, CNN is used to determine semantic categories of ambiguous words. Training corpus of SemEval-2007:Task#5 and semantic annotation corpus from Harbin Institute of Technology are used to optimize CNN classifier. Testing corpus of SemEval-2007:Task#5 is used to test the performance of WSD classifier. Average disambiguation accuracy of the proposed method is improved. Experiments show that this method is feasible in WSD.

Key words: word sense disambiguation, convolution neural network, disambiguation features, semantic categories

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