Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2018, Vol. 54 ›› Issue (2): 262-270.DOI: 10.13209/j.0479-8023.2017.157

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Domain Term Extraction Using URL-Key

LÜ Shuning1, DONG Zhian2,†   

  1. 1. School of Software Engineering, Beijing University of Technology, Beijing 100124
    2. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101
  • Received:2017-06-05 Revised:2017-09-02 Online:2018-03-20 Published:2018-03-20
  • Contact: DONG Zhian, E-mail: dong.zhian(at)


吕书宁1, 董志安2,†   

  1. 1. 北京工业大学软件学院, 北京100124
    2. 北京信息科技大学网络文化与数字传播北京市重点实验室, 北京100101
  • 通讯作者: 董志安, E-mail: dong.zhian(at)
  • 基金资助:


A new approach was presented for domain term extraction using URL-Key. With the help of known URL-Key’s domain, unknown URL-Key’s domain can be identified. First, according to the frequency of URL-Key appearing in various fields, a method based on the variance was proposed to identify the domain URL-Key and build the dictionary of domain URL-Key. Then, the pseudo related feedback was used to construct the URL-Key vector of candidate domain terms. Finally, SVM was applied to extract terms. Experiment was conducted on four different domains for Chinese term extraction. Experimental results indicate that the proposed method is quiet effective. In addition, it can effectively solve the recognition problem of low frequency terms, and provides a new way for the identification of low frequency terms.

Key words: URL, URL-Key, domain term, low-frequency term, SVM


首次提出利用URL-Key进行领域术语识别的方法。以URL作为媒介, 借助已知URL-Key的领域性来判断未知领域候选术语的领域性。首先, 借助互联网中已有的人工分类领域URL, 根据URL-Key在各领域汇总使用的频度, 采用基于方差的领域URL-Key识别方法, 构建领域URL-Key词表; 然后, 利用伪反馈技术, 收集候选领域词检索得到的URL结果集, 根据URL结果集构建候选领域术语的URL-Key特征向量; 最后, 利用SVM对候选领域术语进行提取。在4个领域进行实验, 都取得不错的效果。新提出的方法可以有效地解决低频术语识别问题, 为低频术语的识别提供新的思路。

关键词: URL, URL-Key, 领域术语, 低频术语, SVM

CLC Number: