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Approaching SMM4H with Merged Models and Multi-task Learning


Ellendorff, Tilia; Furrer, Lenz; Colic, Nicola; Aepli, Noëmi; Rinaldi, Fabio (2019). Approaching SMM4H with Merged Models and Multi-task Learning. In: ACL 2019 FOURTH CONFERENCE ON MACHINE TRANSLATION (WMT19), Florence, Italy, 1 August 2019 - 2 August 2019. ACL, 58-61.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:2 August 2019
Deposited On:05 Sep 2019 14:34
Last Modified:14 Aug 2022 08:09
Publisher:ACL
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://www.aclweb.org/anthology/W19-3208/
Project Information:
  • : FunderInnosuisse
  • : Grant ID25587.2 PFES-ES
  • : Project TitleMedMon – Monitoring of web resources for pharmaceutical research and development

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