Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention

Orest Xherija


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
Editors:
Graciela Gonzalez-Hernandez, Davy Weissenbacher, Abeed Sarker, Michael Paul
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-5910.pdf
Code
 orestxherija/smm4h2018