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        <title>Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts</title>
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        <namePart type="given">Emmanouil</namePart>
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        <namePart type="given">Selene</namePart>
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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-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.</abstract>
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%0 Conference Proceedings
%T Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts
%A Manousogiannis, Emmanouil
%A Mesbah, Sepideh
%A Bozzon, Alessandro
%A Baez Santamaria, Selene
%A Sips, Robert Jan
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F manousogiannis-etal-2019-give
%X 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.
%R 10.18653/v1/W19-3219
%U https://aclanthology.org/W19-3219/
%U https://doi.org/10.18653/v1/W19-3219
%P 114-116

Markdown (Informal)

[Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts](https://aclanthology.org/W19-3219/) (Manousogiannis et al., ACL 2019)

• Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts (Manousogiannis et al., ACL 2019)

ACL

• Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Selene Baez Santamaria, and Robert Jan Sips. 2019. Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 114–116, Florence, Italy. Association for Computational Linguistics.