UZH@SMM4H: System Descriptions
Tilia Ellendorff, Joseph Cornelius, Heath Gordon, Nicola Colic, Fabio Rinaldi
Abstract
Our team at the University of Zürich 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.- Anthology ID:
- W18-5916
- Volume:
- Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 56–60
- Language:
- URL:
- https://aclanthology.org/W18-5916
- DOI:
- 10.18653/v1/W18-5916
- Cite (ACL):
- Tilia Ellendorff, Joseph Cornelius, Heath Gordon, Nicola Colic, and Fabio Rinaldi. 2018. UZH@SMM4H: System Descriptions. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 56–60, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- UZH@SMM4H: System Descriptions (Ellendorff et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-5916.pdf