SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improving weak ad-hoc queries using wikipedia asexternal corpus
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Finding good feedback documents
Proceedings of the 18th ACM conference on Information and knowledge management
Explicit extraction of topical context
Journal of the American Society for Information Science and Technology
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
Effective query formulation with multiple information sources
Proceedings of the fifth ACM international conference on Web search and data mining
| Hi-index | 0.00 |
Improving query understanding is crucial for providing the user with information that suits her needs. To this end, the retrieval system must be able to deal with several sources of knowledge from which it could infer a topical context. The use of external sources of information for improving document retrieval has been extensively studied. Improvements with either structured or large sets of data have been reported. However, in these studies resources are often used separately and rarely combined together. We experiment in this paper a method that discounts documents based on their weighted divergence from a set of external resources. We present an evaluation of the combination of four resources on two standard TREC test collections. Our proposed method significantly outperforms a state-of-the-art Mixture of Relevance Models on one test collection, while no significant differences are detected on the other one.