Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs

WS 2017 Sunil MohanNicolas FioriniSun KimZhiyong Lu

We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document{'}s relevance to the query... (read more)

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