@inproceedings{mohan-etal-2017-deep,
title = "Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs",
author = "Mohan, Sunil and
Fiorini, Nicolas and
Kim, Sun and
Lu, Zhiyong",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2328/",
doi = "10.18653/v1/W17-2328",
pages = "222--231",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs
%A Mohan, Sunil
%A Fiorini, Nicolas
%A Kim, Sun
%A Lu, Zhiyong
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 16th BioNLP Workshop
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F mohan-etal-2017-deep
%X 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.
%R 10.18653/v1/W17-2328
%U https://aclanthology.org/W17-2328/
%U https://doi.org/10.18653/v1/W17-2328
%P 222-231
Markdown (Informal)
[Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs](https://aclanthology.org/W17-2328/) (Mohan et al., BioNLP 2017)
ACL