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2018 EMNLP EMNLP 2018

UZH@SMM4H: System Descriptions

Abstract

AbstractOur 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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Tilia Ellendorff , Joseph Cornelius , Heath Gordon , Nicola Colic , Fabio Rinaldi

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks

Keywords

text classification logistic regression support vector machine convolutional neural network recurrent neural network social media mining
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