Chinese Word Sense Disambiguation Based on Maximum Entropy Model with Feature Selection
DOI:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Word sense disambiguation (WSD) can be thought as a classification problem. Feature selection is of great importance in such a task. In general, features are selected manually, which requires a deep understanding of the task itself and the employed classification model. In this paper, the effect of feature template on Chinese WSD is studied, and an automatic feature selection algorithm based on maximum entropy model (MEM) is proposed, including uniform feature template selection for all ambiguous words and customized feature template selection for each word. Experimental result shows that automatic feature selection can reduce feature size and improve Chinese WSD performance. Compared with the best evaluation results of SemEval 2007: task #5, this method gets MicroAve (micro-average accuracy)) increase 3.10% and MacroAve (macro-average accuracy)) 2.96% respectively.

    Reference
    Related
    Cited by
Get Citation

何径舟,王厚峰.基于特征选择和最大熵模型的汉语词义消歧.软件学报,2010,21(6):1287-1295

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:
  • Revised:February 24,2009
  • Adopted:
  • Online:
  • Published:
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063