Passage relevance models for genomics search

We present a passage relevance model for integrating semantic and statistical evidence of biomedical concepts and topics in context using the framework of a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field, and 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 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 demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

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