Semi-Supervised Text Classification

16 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Adversarial Training Methods for Semi-Supervised Text Classification

tensorflow/models 25 May 2016

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.

Deconvolutional Paragraph Representation Learning

dreasysnail/deconv_paragraph_represention NeurIPS 2017

Learning latent representations from long text sequences is an important first step in many natural language processing applications.

Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media

emory-irlab/PHM2017 26 Feb 2018

The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.

Adversarial Dropout for Recurrent Neural Networks

sungraepark/adversarial_dropout_text_classification 22 Apr 2019

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs).

Semi-Supervised Learning with Normalizing Flows

izmailovpavel/flowgmm ICML 2020

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

GT-SALT/MixText ACL 2020

This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.

Variational Pretraining for Semi-supervised Text Classification

allenai/vampire ACL 2019

We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.

Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses

GT-SALT/AAAI_CLF 23 Apr 2020

We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.

Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function

DevSinghSachan/ssl_text_classification AAAI 2019 2019

In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches.