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:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.smm4h-1.1.pdf