Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts

Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Selene Baez, Robert Jan Sips


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-the art 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.
Anthology ID:
W19-3219
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
114–116
Language:
URL:
https://www.aclweb.org/anthology/W19-3219
DOI:
10.18653/v1/W19-3219
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3219.pdf