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.
@article{arxiv.1802.10078,
title = {A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval},
author = {Sunil Mohan and Nicolas Fiorini and Sun Kim and Zhiyong Lu},
journal= {arXiv preprint arXiv:1802.10078},
year = {2018}
}