Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic discovery of language models for text databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Experimentation as a way of life: Okapi at TREC
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
The effectiveness of query expansion for distributed information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Discovering the representative of a search engine
Proceedings of the tenth international conference on Information and knowledge management
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Neural Processing Letters
Distributed information retrieval with skewed database size distributions
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
SINAI at CLEF 2004: using machine translation resources with a mixed 2-step RSV merging algorithm
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Architecture and evaluation of BRUJA, a multilingual question answering system
Information Retrieval
Hi-index | 0.00 |
This paper presents a thorough analysis of the capabilities of the pseudo-relevance feedback (PRF) technique applied to distributed information retrieval (DIR). Previous studies have researched the application of PRF to improve the selection process of the best set of collections from a ranked list. This work emphasizes the effectiveness of PRF applied to the collection fusion problem. Usually, DIR systems apply PRF in the same way as traditional Information Retrieval systems. For each collection, local results are improved through PRF. A first question which arises is whether this local improvement is preserved in the final result. In addition, DIR systems merge the documents of rankings that are returned from a set of collections. Since a new global list of documents is available, we could use that list to apply PRF again, but on global level rather than on a local level. In order to apply global PRF, we have developed a merging approach called two-step RSV. Finally, we describe a number of experiments involving the two levels, local and global, of application of the PRF techniques.