Paper: Document Re-Ranking Based On Automatically Acquired Key Terms In Chinese Information Retrieval

ACL ID C04-1069
Title Document Re-Ranking Based On Automatically Acquired Key Terms In Chinese Information Retrieval
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

For Information Retrieval, users are more concerned about the precision of top ranking documents in most practical situations. In this paper, we propose a method to improve the precision of top N ranking documents by reordering the retrieved documents from the initial retrieval. To reorder documents, we first automatically extract Global Key Terms from document set, then use extracted Global Key Terms to identify Local Key Terms in a single document or query topic, finally we make use of Local Key Terms in query and documents to reorder the initial ranking documents. The experiment with NTCIR3 CLIR dataset shows that an average 10%-11% improvement and 2%-5% improvement in precision can be achieved at top 10 and 100 ranking documents level respectively.