@inproceedings{liu-etal-2018-domain,
title = "Domain Adaptation for Disease Phrase Matching with Adversarial Networks",
author = "Liu, Miaofeng and
Han, Jialong and
Zhang, Haisong and
Song, Yan",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2315",
doi = "10.18653/v1/W18-2315",
pages = "137--141",
abstract = "With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.",
}
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<abstract>With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.</abstract>
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%0 Conference Proceedings
%T Domain Adaptation for Disease Phrase Matching with Adversarial Networks
%A Liu, Miaofeng
%A Han, Jialong
%A Zhang, Haisong
%A Song, Yan
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F liu-etal-2018-domain
%X With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.
%R 10.18653/v1/W18-2315
%U https://aclanthology.org/W18-2315
%U https://doi.org/10.18653/v1/W18-2315
%P 137-141
Markdown (Informal)
[Domain Adaptation for Disease Phrase Matching with Adversarial Networks](https://aclanthology.org/W18-2315) (Liu et al., BioNLP 2018)
ACL