Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
Fast and quasi-natural language search for gigabytes of Chinese texts
SIGIR '95 Proceedings of the 18th 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
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
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Chinese information retrieval based on terms and relevant terms
ACM Transactions on Asian Language Information Processing (TALIP)
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Research on a novel word co-occurrence model and its application
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Expert Systems with Applications: An International Journal
Chinese document re-ranking based on term distribution and maximal marginal relevance
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Document re-ordering based on key terms in top retrieved documents
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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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.