北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2006, Vol. 29 ›› Issue (s2): 88-92.doi: 10.13190/jbupt.2006s2.88.302

• 论文 • 上一篇    下一篇

语言认知与主题内容识别

郭燕慧1,2, 王 枞1, 钟义信1   

  1. 1. 北京邮电大学 信息工程学院, 北京 100876; 2. 北京科技大学 信息工程学院, 北京 100083
  • 收稿日期:2006-09-01 修回日期:1900-01-01 出版日期:2006-11-30 发布日期:2006-11-30
  • 通讯作者: 郭燕慧

Language Cognition and Topic Identification

GUO Yan-hui1,2, WANG Cong1, ZHONG Yi-xin1   

  1. 1. School of Information Engineering , Beijing University of Posts and Telecommunications, Beijing 100876, China; 2. Department of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China)
  • Received:2006-09-01 Revised:1900-01-01 Online:2006-11-30 Published:2006-11-30
  • Contact: GUO Yan-hui

摘要:

在对潜在语义分析和主题模型2种语言模型的基本原理和方法分析的基础上,给出了其在主题内容识别及文本数据挖掘方面的应用前景. 进一步从全信息自然语言理解方法论出发,指出了语言计算研究的突破性进展:一方面需要与认知科学相结合,借鉴认知科学的研究成果;另一方面要重视从语用的维度进行研究.

关键词: 语言认知, 潜在语义分析, 主题模型, 主题识别, 全信息

Abstract:

Based on the analysis of the principle and method of two outstanding language models in recent years, that are Latent Semantic Analysis and Topic Model, the prospects of the two models on topic identification and text mining was presented. And the new strategy for the natural language understanding were proposed on the basis of the Comprehensive Information Natural Language Understanding methodology, that is, should make full use of the research findings of cognitive science nowadays in a new pragmatic perspective, explore the essence of the natural language to realize the computer understanding human’s language indeed.

Key words: language cognition, latent semantic analysis, topic model, topic identification, comprehensive information

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