在SMM4H任务2:从Twitter上收集识别个人药物摄入量的浅层革命神经网络组合 (InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter)

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.

翻译：本文介绍Infosys参与“2017年美洲医学协会健康应用第二社会媒体采矿共同任务”的工作。有关健康和药物相关信息的采矿社会媒体信息在药物监督研究中受到极大关注。这一任务旨在开发自动分类模式,以识别含有个人药物摄入情况说明的推文。为此,我们根据组织者提供的附加说明的数据集,对一组浅层神经神经网络(CNN)模型进行了堆叠式培训。我们利用随机搜索来调整CNN的超参数,并为预测任务提供一套最佳模型。我们的系统在9个团队中占据第一位,拥有0.693个微平均值F分。