Abstract:

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

摘要：

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