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        <title>A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining</title>
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        <namePart type="given">Shweta</namePart>
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    <abstract>The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited amount of ADR relevant information. With the unprecedented growth of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.), biomedical literature, and Electronic Medical Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper, we propose a neural network inspired multi- task learning framework that can simultaneously extract ADRs from various sources. We adopt a novel adversarial learning-based approach to learn features across multiple ADR information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as ‘Indications’, ‘Symptoms’, ‘Finding’, ‘Disease’, ‘Drug’) which provide important cues in pharmacovigilance. We evaluate our proposed approach on three publicly available real- world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. Experiments show that our unified framework achieves state-of-the-art performance on individual tasks associated with the different benchmark datasets. This establishes the fact that our proposed approach is generic, which enables it to achieve high performance on the diverse datasets.</abstract>
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%0 Conference Proceedings
%T A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining
%A Yadav, Shweta
%A Ekbal, Asif
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yadav-etal-2019-unified
%X The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited amount of ADR relevant information. With the unprecedented growth of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.), biomedical literature, and Electronic Medical Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper, we propose a neural network inspired multi- task learning framework that can simultaneously extract ADRs from various sources. We adopt a novel adversarial learning-based approach to learn features across multiple ADR information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as ‘Indications’, ‘Symptoms’, ‘Finding’, ‘Disease’, ‘Drug’) which provide important cues in pharmacovigilance. We evaluate our proposed approach on three publicly available real- world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. Experiments show that our unified framework achieves state-of-the-art performance on individual tasks associated with the different benchmark datasets. This establishes the fact that our proposed approach is generic, which enables it to achieve high performance on the diverse datasets.
%R 10.18653/v1/P19-1516
%U https://aclanthology.org/P19-1516
%U https://doi.org/10.18653/v1/P19-1516
%P 5234-5245

Markdown (Informal)

[A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining](https://aclanthology.org/P19-1516) (Yadav et al., ACL 2019)

• A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (Yadav et al., ACL 2019)

ACL

• Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya. 2019. A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5234–5245, Florence, Italy. Association for Computational Linguistics.