KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish
Antoine Lain, Wonjin Yoon, Hyunjae Kim, Jaewoo Kang, Ian Simpson
Abstract
This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task. We used several types of pre-trained language models, which are trained on Spanish biomedical literature or Spanish Tweets. We showed the difference in performance depending on the quality of the tokenization as well as introducing silver standard annotations when training the model. Our model obtained a strict F1 of 80.3% on the test set, which is an improvement of +12.8% F1 (24.6 std) over the average results across all submissions to the SocialDisNER challenge.- Anthology ID:
- 2022.smm4h-1.23
- 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
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 78–80
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.23
- DOI:
- Cite (ACL):
- Antoine Lain, Wonjin Yoon, Hyunjae Kim, Jaewoo Kang, and Ian Simpson. 2022. KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 78–80, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish (Lain et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.23.pdf