This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-theart approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337- 0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
|Title of host publication||Proceedings of the 4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task|
|Editors||Davy Weissenbacher, Graciela Gonzalez-Hernandez|
|Publisher||Association for Computational Linguistics|
|Number of pages||4|
|Publication status||Published - 2019|
|Event||4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task - Florence, Italy|
Duration: 2 Aug 2019 → 2 Aug 2019
Conference number: 4
|Conference||4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task|
|Period||2/08/19 → 2/08/19|
Manousogiannis, E., Mesbah, S., Baez Santamaria, S., Bozzon, A., & Sips, R-J. (2019). Give it a shot: Few-shot learning to normalize ADR mentions in Social Media posts. In D. Weissenbacher, & G. Gonzalez-Hernandez (Eds.), Proceedings of the 4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task (pp. 114–116). Association for Computational Linguistics.