A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval

Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
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
more » ... rix 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.
doi:10.1145/3178876.3186049 dblp:conf/www/MohanFKL18 fatcat:hceiu7l4lngizbbjeuex5q2yya