Documents

  • W19_3219

    Accepted author manuscript, 149 KB, PDF-document

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.
Original languageEnglish
Title of host publicationProceedings of the 4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
EditorsDavy Weissenbacher, Graciela Gonzalez-Hernandez
PublisherAssociation for Computational Linguistics
Pages114–116
Number of pages4
ISBN (Electronic)978-1-950737-46-8
Publication statusPublished - 2019
Event4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task - Florence, Italy
Duration: 2 Aug 20192 Aug 2019
Conference number: 4
https://www.aclweb.org/anthology/W19-32

Conference

Conference4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Abbreviated title#SMM4H
CountryItaly
CityFlorence
Period2/08/192/08/19
Internet address

ID: 70444657