Majority voting over multiple pretrained BERT models improves medication event classification on the n2c2 CMED dataset, but the paper lacks error bars and code.
Emotion Detection From Tweets Using a BERT and SVM Ensemble Model
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abstract
Automatic identification of emotions expressed in Twitter data has a wide range of applications. We create a well-balanced dataset by adding a neutral class to a benchmark dataset consisting of four emotions: fear, sadness, joy, and anger. On this extended dataset, we investigate the use of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for emotion recognition. We propose a novel ensemble model by combining the two BERT and SVM models. Experiments show that the proposed model achieves a state-of-the-art accuracy of 0.91 on emotion recognition in tweets.
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Ensemble BERT for Medication Event Classification on Electronic Health Records (EHRs)
Majority voting over multiple pretrained BERT models improves medication event classification on the n2c2 CMED dataset, but the paper lacks error bars and code.