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计算机工程 ›› 2011, Vol. 37 ›› Issue (01): 57-59,62. doi: 10.3969/j.issn.1000-3428.2011.01.020

• 软件技术与数据库 • 上一篇    下一篇

基于语义概念的海量短信文本聚类

刘金岭   

  1. (淮阴工学院计算机工程学院,江苏 淮安 223003)
  • 出版日期:2011-01-05 发布日期:2010-12-31
  • 作者简介:刘金岭(1958-),男,教授,主研方向:数据仓库,文本数据挖掘
  • 基金资助:
    淮安科技计划基金资助项目“基于语义的垃圾短信分类器设计与实现”(HAG09061);淮阴工学院自然科学重点基金资助项目“短信文本智能分类主题提取研究”(HGA0907)

Clustering on Mass Chinese Short Message Text Based on Semantic Concept

LIU Jin-ling   

  1. (Computer Engineering Faculty, Huaiyin Institute of Technology, Huai’an 223003, China)
  • Online:2011-01-05 Published:2010-12-31

摘要: 提出一种基于语义概念的海量中文短信文本聚类方法。该方法从短信文本出发,利用《现代汉语语义分类词典》的级类主题词,在短信文本向量集中提取概念元组,形成表示聚类结果的高层概念,基于这些高层概念进行样本划分,从而完成整个聚类过程。实验结果表明,该聚类算法有较好的聚类结果且执行效率较高。

关键词: 短信文本, 概念元组, 聚类

Abstract: This paper puts forward a clustering on mass Chinese short message texts technique based on semantic concept. The method starts from the Chinese short message itself, it uses classified hierarchy Subject Word of Thesaurus of Modern Chinese, then abstracts conceptional tuple from short message text, forms clustering result expressed by high-leveled concept, based on which these samples are divided, thus the whole clustering process is completed. Experimental result shows the clustering algorithm achieves a satisfactory clustering result and a better executive efficiency as well.

Key words: short message text, conceptional tuple, clustering

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