Leveraging knowledge sources for detecting self-reports of particular health issues on social media

Parsa Bagherzadeh, Sabine Bergler


Abstract
This paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories. We claim that resources such as the MeSH hierarchy and currently available parse information are effective extensions of moderately sized training datasets for various fine-grained tweet classification tasks of self-reported health issues.
Anthology ID:
2021.louhi-1.5
Volume:
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Month:
April
Year:
2021
Address:
online
Venues:
EACL | Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–48
Language:
URL:
https://aclanthology.org/2021.louhi-1.5
DOI:
Bibkey:
Cite (ACL):
Parsa Bagherzadeh and Sabine Bergler. 2021. Leveraging knowledge sources for detecting self-reports of particular health issues on social media. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pages 38–48, online. Association for Computational Linguistics.
Cite (Informal):
Leveraging knowledge sources for detecting self-reports of particular health issues on social media (Bagherzadeh & Bergler, Louhi 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.louhi-1.5.pdf
Data
SMM4H