Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (01): 182-185.DOI: 10.3724/SP.J.1087.2013.00182

• Artificial intelligence • Previous Articles     Next Articles

Chinese story link detection based on extraction of elements correlative word

CHEN Zhimin,MENG Zuqiang,LIN Qifeng   

  1. College of Computer, Electronics and Information, Guangxi University, Nanning Guangxi 530004, China
  • Received:2012-07-23 Revised:2012-08-23 Online:2013-01-01 Published:2013-01-09
  • Contact: CHEN Zhimin



  1. 广西大学 计算机与电子信息学院, 南宁 530004
  • 通讯作者: 陈智敏
  • 作者简介:陈智敏(1988-),女,广西北海人,硕士研究生,主要研究方向:数据挖掘、智能系统与智能CAD;蒙祖强(1974-),男(壮族),广西罗城人,教授,博士,主要研究方法:人工智能、数据挖掘;林啟锋(1987-),男,福建三明人,硕士研究生,主要研究方向:数据挖掘。
  • 基金资助:


Abstract: At present, the cost of Chinese story link detection is high,since the miss rate and false rate are high. Concerning this problem, based on multi-vector space model, the paper joined elements (time, site, people, content) correlative word to represent the relevance of the different elements, integrated coherence similarity and cosine similarity with Support Vector Machine (SVM), and then proposed an algorithm which was based on the extraction of elements correlative word. The proposed algorithm complementally expressed the story and provided more evidence for detection; the detection cost was decreased by nearly 11%. Finally, the experimental results show the validity of the proposed algorithm.

Key words: story link detection, correlative word, story elements, multi-vector space model

摘要: 针对现有中文报道关系检测的检测代价即误报率和丢失率较高的问题,在多向量空间模型基础上提取不同向量的要素(时间、地点、人物和内容)特征词组成关联词对,使用支持向量机(SVM)方法整合关联词对相似度和余弦相似度,从而提出了一种提取要素关联词对报道关系检测方法。所提方法补充表示了报道内容,为检测提供了更多的比较依据,识别代价降低了将近11%。实验结果验证了算法的有效性。

关键词: 报道关系识别检测, 关联词对, 报道要素, 多向量空间模型

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