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Approaching SMM4H with Merged Models and Multi-task Learning

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Ellendorff Tilia, Furrer Lenz, Colic Nicola, Aepli Noëmi, Rinaldi Fabio,
Project MelanoBase
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Proceedings (peer-reviewed)

Page(s) 58 - 61
Title of proceedings Proceedings of the 4th Social Media Mining for Health Applications (\#SMM4H) Workshop & Shared Task


We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.