计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (3): 119-122.

• 模式识别与人工智能 • 上一篇    下一篇

结合语义知识的汉语词义消歧

张春祥1,2,邓  龙3,高雪瑶3,卢志茂2   

  1. 1.哈尔滨理工大学 软件学院,哈尔滨 150080
    2.哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001
    3.哈尔滨理工大学 计算机科学与技术学院,哈尔滨 150080
  • 出版日期:2016-02-01 发布日期:2016-02-03

Chinese word sense disambiguation with semantic knowledge

ZHANG Chunxiang1,2, DENG Long3, GAO Xueyao3, LU Zhimao2   

  1. 1.School of Software, Harbin University of Science and Technology, Harbin 150080, China
    2.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
    3.School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Online:2016-02-01 Published:2016-02-03

摘要: 词义消歧一直是自然语言处理领域中的关键性问题。为了提高词义消歧的准确率,从目标歧义词汇出发,挖掘左右词单元的语义知识。以贝叶斯模型为基础,结合左右词单元的语义信息,提出了一种新的词义消歧方法。以SemEval-2007:Task#5作为训练语料和测试语料,对词义消歧分类器进行优化,并对优化后的分类器进行测试。实验结果表明:词义消歧的准确率有所提高。

关键词: 词义消歧, 歧义词汇, 贝叶斯模型, 语义信息

Abstract: Word sense disambiguation is an important problem in natural language processing. In order to improve the precision of word sense disambiguation, semantic knowledge of left and right word units is mined starting from the target polysemous word. Based on the Bayesian model, a new method of word sense disambiguation is proposed with semantic information of left and right word units. SemEval-2007:Task#5 is used as training corpus and test corpus. The classifier of word sense disambiguation is optimized. Then the optimized classifier is tested. Experimental results show that the precision of word sense disambiguation is improved.

Key words: word sense disambiguation, polysemous word, Bayesian model, semantic information