InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter
This work addresses the need for automated classification in pharmacovigilance research by mining social media data, but it is incremental as it applies an ensemble of existing CNN methods to a specific shared task.
The paper tackled the problem of automatically identifying tweets describing personal medication intake by training a stacked ensemble of shallow convolutional neural networks, achieving first place among 9 teams with a micro-averaged F-score of 0.693.
This paper describes Infosys's participation in the "2nd Social Media Mining for Health Applications Shared Task at AMIA, 2017, Task 2". Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. This task targets at developing automated classification models for identifying tweets containing descriptions of personal intake of medicines. Towards this objective we train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset provided by the organizers. We use random search for tuning the hyper-parameters of the CNN and submit an ensemble of best models for the prediction task. Our system secured first place among 9 teams, with a micro-averaged F-score of 0.693.