Passage relevance models for genomics search

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Passage relevance models for genomics search
We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus. Ba...
Jay Urbain, Ophir Frieder, Nazli Goharian
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where CIKM
Authors Jay Urbain, Ophir Frieder, Nazli Goharian
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