@inproceedings{yadav-etal-2018-multi,
title = "Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment",
author = "Yadav, Shweta and
Ekbal, Asif and
Saha, Sriparna and
Bhattacharyya, Pushpak and
Sheth, Amit",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2044",
doi = "10.18653/v1/N18-2044",
pages = "271--277",
abstract = "In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user{'}s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.",
}
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<abstract>In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user’s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment’s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment
%A Yadav, Shweta
%A Ekbal, Asif
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%A Sheth, Amit
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F yadav-etal-2018-multi
%X In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user’s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment’s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.
%R 10.18653/v1/N18-2044
%U https://aclanthology.org/N18-2044
%U https://doi.org/10.18653/v1/N18-2044
%P 271-277
Markdown (Informal)
[Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment](https://aclanthology.org/N18-2044) (Yadav et al., NAACL 2018)
ACL