Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Effectiveness of query expansion in ranked-output document retrieval systems
Journal of Information Science
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th 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)
Information retrieval on the web
ACM Computing Surveys (CSUR)
Text Retrieval Systems for the Web
Programming and Computing Software
The role of manually-assigned keywords in query expansion
Information Processing and Management: an International Journal
Optimization of some factors affecting the performance of query expansion
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
The ever growing popularity of the Internet as a source of information, coupled with the accompanying growth in the number of documents available through the World Wide Web, is leading to an increasing demand for more efficient and accurate information retrieval tools. One of the fundamental problems in information retrieval is word mismatch. Expanding a user’s query with related words can improve the search performance, but the finding and using of related words is still an open problem. On the basis of previous approaches to query expansion, this paper proposes a new approach to query expansion that combines two popular traditional methods—thesauri and automatic relevance feedback. According to theoretical analysis and experiments, the new approach can effectively improve the web retrieval performance and out-performs the optimized conventional expansion approaches.