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This paper describes Infosys's participation in the "2nd Social Media Miningfor Health Applications Shared Task at AMIA, 2017, Task 2". Mining social mediamessages for health and drug related information has received significantinterest in pharmacovigilance research. This task targets at developingautomated classification models for identifying tweets containing descriptionsof personal intake of medicines. Towards this objective we train a stackedensemble of shallow convolutional neural network (CNN) models on an annotateddataset provided by the organizers. We use random search for tuning thehyper-parameters of the CNN and submit an ensemble of best models for theprediction task. Our system secured first place among 9 teams, with amicro-averaged F-score of 0.693.

InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter