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        <title>HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets</title>
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        <namePart type="given">Shuai</namePart>
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        <namePart type="given">Yuanhang</namePart>
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        <namePart type="given">Haoming</namePart>
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    <abstract>This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019. The two subtasks are automatic classification and extraction of adverse effect mentions in tweets. The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results. Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively. Our system ranks first among all systems on subtask1.</abstract>
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%0 Conference Proceedings
%T HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets
%A Chen, Shuai
%A Huang, Yuanhang
%A Huang, Xiaowei
%A Qin, Haoming
%A Yan, Jun
%A Tang, Buzhou
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-hitsz
%X This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019. The two subtasks are automatic classification and extraction of adverse effect mentions in tweets. The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results. Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively. Our system ranks first among all systems on subtask1.
%R 10.18653/v1/W19-3206
%U https://aclanthology.org/W19-3206
%U https://doi.org/10.18653/v1/W19-3206
%P 47-51

Markdown (Informal)

[HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets](https://aclanthology.org/W19-3206) (Chen et al., ACL 2019)

• HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets (Chen et al., ACL 2019)

ACL

• Shuai Chen, Yuanhang Huang, Xiaowei Huang, Haoming Qin, Jun Yan, and Buzhou Tang. 2019. HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 47–51, Florence, Italy. Association for Computational Linguistics.