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
- Editors:
- Davy Weissenbacher, Graciela Gonzalez-Hernandez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 120–122
- Language:
- URL:
- https://aclanthology.org/W19-3221
- DOI:
- 10.18653/v1/W19-3221
- Cite (ACL):
- Sarah Sarabadani. 2019. Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 120–122, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo (Sarabadani, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-3221.pdf
- Data
- SMM4H