Semi-Supervised Text Classification
22 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Text Classification
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AG News
HeriGraph
Latest papers with no code
Progressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification.
ME-GCN: Multi-dimensional Edge-Enhanced Graph Convolutional Networks for Semi-supervised Text Classification
Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus.
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks.
Data Augmentation with Sentence Recombination Method for Semi-supervised Text Classification
As the need of large amount of time and expertise to obtain enough labeled data, semi-supervised learning has received much attention to utilize both labeled and unlabeled data.
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus.
Semi-Supervised Text Classification with Balanced Deep Representation Distributions
They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts.
A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents
We improve the performance significantly by evolving the model from multiclass classification to semi-supervised multi-task learning by leveraging the negative cases, domain- and task-adaptively pretrained ALBERT on customer contact texts, and a number of un-curated data with no labels.
Inductive Topic Variational Graph Auto-Encoder for Text Classification
T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
That is, even if the model using our VAT-based technique is trained on unlabeled data from a source other than the target task, both the prediction performance and model interpretability can be improved.
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC).
