计算机工程 ›› 2012, Vol. 38 ›› Issue (22): 130-132.doi: 10.3969/j.issn.1000-3428.2012.22.032

所属专题: 机器学习

• 人工智能及识别技术 • 上一篇    下一篇

用于中文缺省识别研究的机器学习方法

秦凯伟 1,2,孔 芳 1,2,李培峰 1,2,朱巧明 1,2,徐生芹 1,2   

  1. (1. 苏州大学计算机科学与技术学院,江苏 苏州 215006;2. 江苏省计算机信息处理技术重点实验室,江苏 苏州 215006)
  • 收稿日期:2012-02-29 修回日期:2012-03-20 出版日期:2012-11-20 发布日期:2012-11-17
  • 作者简介:秦凯伟(1987-),男,硕士研究生,主研方向:自然语言处理;孔 芳、李培峰,副教授;朱巧明,教授、博士生导师;徐生芹,硕士研究生
  • 基金项目:
    国家自然科学基金资助项目(90920004, 60970056, 61070123, 61003153);江苏省高校自然科学重大基础研究基金资助项目(08KJA520002);苏州市科技计划基金资助项目(SYG201112)

Machine Learning Approach for Chinese Ellipsis Identification Study

QIN Kai-wei 1,2, KONG Fang 1,2, LI Pei-feng 1,2, ZHU Qiao-ming 1,2, XU Sheng-qin 1,2   

  1. (1. School of Computer Science & Technology, Soochow University, Suzhou 215006, China; 2. Key Lab of Computer Information Processing Technology of Jiangsu Province, Suzhou 215006, China)
  • Received:2012-02-29 Revised:2012-03-20 Online:2012-11-20 Published:2012-11-17

摘要: 实现一个基于机器学习的中文缺省项识别系统,对语料库进行预处理,选取多个特征及其组合,通过支持向量模型(SVM)构建的缺省识别模型进行中文缺省识别。研究系统在不同句法分析树上的性能。实验结果证明,该识别系统在标准的句法分析树上F值能达到84.01%,在自动句法树上能达到68.22%。

关键词: 缺省, 自然语言处理, 句法分析树, 机器学习, 语料, 缺省识别

Abstract: This paper presents a system for ellipsis identification in Chinese which is based on machine learning. The system can be used to select a number of features and feature combinations through preprocessing the corpus. And Chinese ellipsis identification can also be achieved by the ellipsis identification model built by Support Vector Machine(SVM). The performance of the system in different parser tree is studied as well. Experimental result shows that the system has F value of 84.01% on the standard parser tree, and 68.22% on automatic sentence parser tree.

Key words: ellipsis, natural language processing, sentence parse tree, machine learning, corpus, ellipsis identification

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