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://aclanthology.org/W18-5911
DOI:
10.18653/v1/W18-5911
Bibkey:
Cite (ACL):
Aditya Joshi, Xiang Dai, Sarvnaz Karimi, Ross Sparks, Cécile Paris, and C Raina MacIntyre. 2018. Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 43–47, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection (Joshi et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5911.pdf
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