In this paper, we present an approach to query modeling that leverages the temporal distribution of documents in an initially retrieved set of documents.
We present an approach to query modeling that uses the temporal distribution of documents in an initially retrieved set of documents. Such distributions tend to exhibit bursts, especially in news related document collections. We hypothesize that documents in those bursts are more likely to be relevant than others. Predicated on…
We describe the participation of the University of Amsterdam’s ILPS group in the blog, enterprise and relevance feedback track at TREC 2008. Our main preliminary conclusions are that estimating mixture weights for external expansion in blog post retrieval is non-trivial and we need more analysis to find out why it…
Relevance feedback is often applied to better capture a user’s information need. Automatically reformulating queries (or blind relevance feedback) entails looking at the terms in some set of (pseudo-)relevant documents and selecting the most informative ones with respect to the set or the collection. These terms may then be reweighed…