Semi-Supervised Text Classification
19 papers with code • 2 benchmarks • 2 datasets
We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself.
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks.
We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.