Give it a shot: Few-shot learning to normalize ADR mentions in Social Media posts

Conference paper (2019)

Authors

E. Manousogiannis Student, myTomorrows
S. Mesbah Web Information Systems - EEMCS
A. Bozzon Web Information Systems - EEMCS , Human-Centred Artificial Intelligence - IDE
Robert-Jan Sips myTomorrows
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2019 E. Manousogiannis, S. Mesbah, Selene Baez Santamaria, A. Bozzon, Robert-Jan Sips
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.

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