  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 Delta matrix between the query and document,
representing a difference between the two texts, which is then passed through a
deep convolution stage followed by a deep feed-forward network to compute a
relevance score. This results in a fast model suitable for use in an online
search engine. The model is robust and outperforms comparable state-of-the-art
deep learning approaches.
