@inproceedings{mahata-etal-2019-midas-smm4h,
title = "{MIDAS}@{SMM}4{H}-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from {T}witter",
author = "Mahata, Debanjan and
Anand, Sarthak and
Zhang, Haimin and
Shahid, Simra and
Mehnaz, Laiba and
Kumar, Yaman and
Shah, Rajiv Ratn",
editor = "Weissenbacher, Davy and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3223",
doi = "10.18653/v1/W19-3223",
pages = "127--132",
abstract = "In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.",
}
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<abstract>In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.</abstract>
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%0 Conference Proceedings
%T MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter
%A Mahata, Debanjan
%A Anand, Sarthak
%A Zhang, Haimin
%A Shahid, Simra
%A Mehnaz, Laiba
%A Kumar, Yaman
%A Shah, Rajiv Ratn
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F mahata-etal-2019-midas-smm4h
%X In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.
%R 10.18653/v1/W19-3223
%U https://aclanthology.org/W19-3223
%U https://doi.org/10.18653/v1/W19-3223
%P 127-132
Markdown (Informal)
[MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter](https://aclanthology.org/W19-3223) (Mahata et al., ACL 2019)
ACL