On filtering irrelevant results in peer-to-peer search
Proceedings of the 2008 ACM symposium on Applied computing
Short and informal documents: a probabilistic model for description enrichment
NGITS'09 Proceedings of the 7th international conference on Next generation information technologies and systems
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Interactive and context-aware tag spell check and correction
Proceedings of the 21st ACM international conference on Information and knowledge management
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Previously [2], we postulated the advantage of using entity extraction to implement a new Peer-to-Peer (P2P) search framework for reducing network traffic and providing a trade off between precision and recall. We now propose an entity ranking method designed for the 'short documents' characteristic of P2P, which significantly improves both precision and recall in 'top results' P2P search. We construct a dynamic entity corpus using n-grams statistics and metadata, study its reliability, and use it to identify correlations between user query terms.