@inproceedings{wunnava-etal-2020-dual,
title = "A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events",
author = "Wunnava, Susmitha and
Qin, Xiao and
Kakar, Tabassum and
Kong, Xiangnan and
Rundensteiner, Elke",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.306/",
doi = "10.18653/v1/2020.findings-emnlp.306",
pages = "3414--3423",
abstract = "An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5{\%} increase) and (ii) ADE sentence classification (13.6{\%} increase) on MADE 1.0 benchmark of EHR notes."
}
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<abstract>An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes.</abstract>
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%0 Conference Proceedings
%T A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
%A Wunnava, Susmitha
%A Qin, Xiao
%A Kakar, Tabassum
%A Kong, Xiangnan
%A Rundensteiner, Elke
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wunnava-etal-2020-dual
%X An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes.
%R 10.18653/v1/2020.findings-emnlp.306
%U https://aclanthology.org/2020.findings-emnlp.306/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.306
%P 3414-3423
Markdown (Informal)
[A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events](https://aclanthology.org/2020.findings-emnlp.306/) (Wunnava et al., Findings 2020)
ACL