Abstract: For the problem of query classification, a variance based method is proposed to identify domain URL-key by the domain URL organized manually from aggregator sites and the use frequency of URL-key in each category. Then, the URL-key is filtered by using machine translation, pinyin and search results feedback technology. Finally, coupled with relevance feedback, the authors classify the query by selecting the URL-key as feature and establishing the URL-key vector with a SVM multi-class classifier. Experimental results show that the proposed method uses less resources and the F-value is 7% higher than contrast method.

Key words: query classification, URL, URL-key, pseudo relevance feedback, query classification, URL, URL-key, pseudo relevance feedback

摘要： 针对查询分类问题, 借助互联网中人工组织的分类网站领域URL, 利用URL-key在各个类别中使用的频度, 提出基于方差的领域URL-key识别方法, 利用机器翻译、拼音翻译和搜索结果反馈等技术对URL-key进行过滤, 构建领域URL-key。然后结合伪相关反馈技术, 选取URL-key为特征, 构建URL-key向量, 利用SVM对查询串进行分类。实验结果表明, 该方法不仅F值比对比方法提高7%, 而且资源的使用也远远小于对比方法, 提高了系统的时效性。

关键词: 查询分类, URL, URL-key, 伪相关反馈, 查询分类, URL, URL-key, 伪相关反馈