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UZH@SMM4H: System Descriptions


Ellendorff, Tilia; Cornelius, Joseph; Gordon, Heath; Colic, Nicola; Rinaldi, Fabio (2018). UZH@SMM4H: System Descriptions. In: SMM4H: The 3rd Social Media Mining for Health Applications Workshop and Shared Task, Brussels, 31 November 2018 - 31 November 2018, 56-60.

Abstract

Our team at the University of Zurich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.

Abstract

Our team at the University of Zurich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.

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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
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:31 November 2018
Deposited On:15 Feb 2019 15:33
Last Modified:25 Sep 2019 00:27
Publisher:Association for Computational Linguistics
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://aclweb.org/anthology/W18-5916

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