Abstract
This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling Tasks 1, 2, 5, 6 and 10. The main takeaways we got from participating in different tasks are: the overwhelming positive effects of combining different architectures when using ensemble learning, and the great potential of generative models for term normalization.
Original language | English |
---|---|
Title of host publication | Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task (SMM4H 2022) |
Editors | Graciela Gonzalez-Hernandez, Davy Weissenbacher |
Pages | 130–134 |
Publication status | Published - Oct 2022 |
Event | The 29th International Conference on Computational Linguistics - Gyeongju, Korea, Democratic People's Republic of Duration: 12 Oct 2022 → 17 Oct 2022 http://coling2022.org/ |
Conference
Conference | The 29th International Conference on Computational Linguistics |
---|---|
Abbreviated title | COLING2022 |
Country/Territory | Korea, Democratic People's Republic of |
City | Gyeongju |
Period | 12/10/22 → 17/10/22 |
Internet address |