@InProceedings{yadav-EtAl:2018:N18-2,
  author    = {Yadav, Shweta  and  Ekbal, Asif  and  Saha, Sriparna  and  Bhattacharyya, Pushpak  and  Sheth, Amit},
  title     = {Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-2044}
}