2018/02/26 by Mohan, Sunil, Fiorini, Nicolas, Kim, Sun +1 · 1 citation
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR)
paper · doi:10.48550/arxiv.1802.10078
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.