Automatic text processing
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
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
Combining multiple evidence from different properties of weighting schemes
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
19th International Conference on Research Development in Information Retrieval
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining Multiple Evidence from Different Relevant Feedback Networks
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
| Hi-index | 0.00 |
Expanding a user query automatically with terms taken from documents that are most similar to the query is a reliable way of finding more relevant documents. To date most approaches to this problem have focused on modifying the query. In this paper we argue that it is useful to create a new query from similar documents, rank both the user query and the new query, and combine the evidence. We show that there are both theoretic and practical advantages in this process.