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
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–42
Language:
URL:
https://www.aclweb.org/anthology/W18-5910
DOI:
10.18653/v1/W18-5910
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5910.pdf