Semi-Supervised Text Classification
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Benchmarks
AG News (200 Labels)
Most implemented
Deconvolutional Paragraph Representation Learning
Adversarial Training Methods for Semi-Supervised Text Classification
Rethinking Semi-supervised Learning with Language Models
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
Semi-Supervised Learning with Normalizing Flows
Papers
Semi-Supervised Learning with Balanced Deep Representation Distributions
Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseud…
Semi-Supervised Text ClassificationMulti-Label ClassificationMulti-class ClassificationMultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification
We introduce MultiMatch, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a three-fold pseudo-l…
Pseudo LabelSemi-Supervised Text Classificationtext-classificationText ClassificationThe Efficiency of Pre-training with Objective Masking in Pseudo Labeling for Semi-Supervised Text Classification
We extend and study a semi-supervised model for text classification proposed earlier by Hatefi et al. for classification tasks in which document classes are described by a small number of gold-labeled examples, while the…
Semi-Supervised Text Classificationtext-classificationText ClassificationUnsupervised Pre-trainingSemantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis
Accurate sentiment analysis of texts is crucial for a variety of applications, such as understanding customer feedback, monitoring market trends, and detecting public sentiment. However, manually annotating large sentime…
Semi-Supervised Text ClassificationSentiment Analysistext-classificationText ClassificationContrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification
Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of …
Contrastive LearningGraph AttentionGraph LearningRepresentation Learning+3Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs
This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets wit…
ClassificationClusteringData AugmentationDocument Classification+10