Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs

Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu


Abstract
We describe a Deep Learning approach to modeling the relevance of a document’s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document’s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.
Anthology ID:
W17-2328
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
222–231
Language:
URL:
https://www.aclweb.org/anthology/W17-2328
DOI:
10.18653/v1/W17-2328
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2328.pdf