RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism

Andrei-Marius Avram, Vasile Pais, Maria Mitrofan


Abstract
This paper presents our system employed for the Social Media Mining for Health (SMM4H) 2022 competition Task 10 - SocialDisNER. The goal of the task was to improve the detection of diseases in tweets. Because the tweets were in Spanish, we approached this problem using a system that relies on a pre-trained multilingual model and is fine-tuned using the recently introduced lateral inhibition layer. We further experimented on this task by employing a conditional random field on top of the system and using a voting-based ensemble that contains various architectures. The evaluation results outlined that our best performing model obtained 83.7% F1-strict on the validation set and 82.1% F1-strict on the test set.
Anthology ID:
2022.smm4h-1.1
Volume:
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–3
Language:
URL:
https://aclanthology.org/2022.smm4h-1.1
DOI:
Bibkey:
Cite (ACL):
Andrei-Marius Avram, Vasile Pais, and Maria Mitrofan. 2022. RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 1–3, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism (Avram et al., SMM4H 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.smm4h-1.1.pdf