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    <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.</abstract>
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%0 Conference Proceedings
%T Leveraging knowledge sources for detecting self-reports of particular health issues on social media
%A Bagherzadeh, Parsa
%A Bergler, Sabine
%S Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
%D 2021
%8 apr
%I Association for Computational Linguistics
%C online
%F bagherzadeh-bergler-2021-leveraging
%X 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.
%U https://aclanthology.org/2021.louhi-1.5
%P 38-48

Markdown (Informal)

[Leveraging knowledge sources for detecting self-reports of particular health issues on social media](https://aclanthology.org/2021.louhi-1.5) (Bagherzadeh & Bergler, Louhi 2021)

• Leveraging knowledge sources for detecting self-reports of particular health issues on social media (Bagherzadeh & Bergler, Louhi 2021)

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.