Abstract
Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.- Anthology ID:
- W19-3215
- 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:
- 99–101
- Language:
- URL:
- https://aclanthology.org/W19-3215
- DOI:
- 10.18653/v1/W19-3215
- Cite (ACL):
- Paul Barry and Ozlem Uzuner. 2019. Deep Learning for Identification of Adverse Effect Mentions In Twitter Data. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 99–101, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Deep Learning for Identification of Adverse Effect Mentions In Twitter Data (Barry & Uzuner, ACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W19-3215.pdf