Abstract
This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.- Anthology ID:
- W18-5910
- Volume:
- Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–42
- Language:
- URL:
- https://aclanthology.org/W18-5910
- DOI:
- 10.18653/v1/W18-5910
- Cite (ACL):
- Orest Xherija. 2018. Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 38–42, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention (Xherija, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-5910.pdf
- Code
- orestxherija/smm4h2018