Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Comparing representations in Chinese information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
On the use of words and n-grams for Chinese information retrieval
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
Improving the retrieval effectiveness of very short queries
Information Processing and Management: an International Journal
Re-ranking method based on inter-document distances
Information Processing and Management: an International Journal
Document re-ranking based on automatically acquired key terms in Chinese information retrieval
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Improving retrieval effectiveness by using key terms in top retrieved documents
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Document re-ranking using cluster validation and label propagation
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Automatic query expansion using data manifold
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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In this paper, we propose a document re-ranking method for Chinese information retrieval where a query is a short natural language description. The method bases on term distribution where each term is weighted by its local and global distribution, including document frequency, document position and term length. The weight scheme lifts off the worry that very fewer relevant documents appear in top retrieved documents, and allows randomly setting a larger portion of the retrieved documents as relevance feedback. It also helps to improve the performance of MMR model in document re-ranking. The experiments show our method can get significant improvement against standard baselines, and outperforms relevant methods consistently.