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Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept. Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query. Limited by a relatively small amount of training data, the model uses pre-trained word embeddings. With these, the model first computes a variable-length Deltadoi:10.1145/3178876.3186049 dblp:conf/www/MohanFKL18 fatcat:hceiu7l4lngizbbjeuex5q2yya