Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings

Segun Taofeek Aroyehun, Alexander Gelbukh

Abstract
This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.
Anthology ID:
W19-3224
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Davy Weissenbacher, Graciela Gonzalez-Hernandez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
133–135
Language:
URL:
https://aclanthology.org/W19-3224
DOI:
10.18653/v1/W19-3224
Bibkey:
Cite (ACL):
Segun Taofeek Aroyehun and Alexander Gelbukh. 2019. Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 133–135, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings (Aroyehun & Gelbukh, ACL 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/W19-3224.pdf
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