Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo

Sarah Sarabadani


Abstract
This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
Anthology ID:
W19-3221
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
120–122
Language:
URL:
https://www.aclweb.org/anthology/W19-3221
DOI:
10.18653/v1/W19-3221
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3221.pdf