Chinese Query Expansion Based on Topic-Relevant Terms

摘要：

In this paper we present a Chinese query expansion model based on topic-relevant terms which were acquired from the Google search engine automatically. In contrast to earlier methods, our queries are expanded by adding those terms that are most relevant to the concept of the query, rather than selecting terms that are relevant to the query terms. Firstly, we use automatically extracted short terms from document sets to build indexes and use the short terms in both the query and documents to do initial retrieval. Next, we acquire the topic-relevant terms of the short terms from the Internet and the top 30 initial retrieval documents. Finally, we use the topic-relevant terms to do query expansion. The experiments show that our query expansion model is more effective than the standard Rocchio expansion.

关键词： query expansion relevant terms information retrieval

作者: Xinhui TU Tingting HE Jing LUO JingGuang CHEN Long CHEN Zongkai YANG

作者单位: Engineering & Research Center For Information Technology On Education,Huazhong Normal University.Wuh Engineering & Research Center For Information Technology On Education,Huazhong Normal University.Wuh School of Computer Science and Technology,Wuhan University of Science and Technology.Wuhan,Hubei,Chi

会议类型: 国际会议

会议名称: The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-19（万方平台首次上网日期，不代表论文的发表时间）