Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection

Aditya Joshi, Xiang Dai, Sarvnaz Karimi, Ross Sparks, Cécile Paris, C Raina MacIntyre


Abstract
Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.
Anthology ID:
W18-5911
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:
43–47
Language:
URL:
https://www.aclweb.org/anthology/W18-5911
DOI:
10.18653/v1/W18-5911
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5911.pdf