Give it a shot: Few-shot learning to normalize ADR mentions in Social Media posts
E. Manousogiannis (Student TU Delft, myTomorrows)
Sepideh Mesbah (TU Delft - Web Information Systems)
Selene Baez Santamaria (myTomorrows)
Alessandro Bozzon (TU Delft - Web Information Systems, TU Delft - Human-Centred Artificial Intelligence)
Robert-Jan Sips (myTomorrows)
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Abstract
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.