Papers Semi-Supervised Text Classification
“Semi-Supervised Text Classification” 태그가 달린 논문 42편 · 필터 해제
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+10Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification
Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating model training. However, existing GCL-ba…
ClassificationClusteringContrastive LearningSemi-Supervised Text Classification+2JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias …
Pseudo LabelSemi-Supervised Text Classificationtext-classificationText ClassificationDeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organizations leverage such information to acq…
Semi-Supervised Text ClassificationSemiReward: A General Reward Model for Semi-supervised Learning
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the c…
Few-Shot Image ClassificationImage ClassificationPseudo LabelSemi-supervised Audio Classification+3Rank-Aware Negative Training for Semi-Supervised Text Classification
Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled text…
ClassificationSemi-Supervised Text Classificationtext-classificationText ClassificationRethinking Semi-supervised Learning with Language Models
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. Currently, there are two popular appro…
Pseudo LabelSemi-Supervised Text ClassificationUnsupervised Pre-trainingDisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks. However, a significant challenge nowadays is maintaining performance when we use a lightweight mod…
Extractive SummarizationKnowledge DistillationLanguage ModelingLanguage Modelling+3SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a Simple instance-Adaptive self-Training method (SAT) for semi-super…
Pseudo LabelSemi-Supervised Text ClassificationText ClassificationProgressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressiv…
General ClassificationLanguage ModelingLanguage ModellingSemi-Supervised Text Classification+1ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks. Most Graph Convolutional Networks are d…
Semi-Supervised Text ClassificationText ClassificationProgressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressiv…
ClassificationLanguage ModelingLanguage ModellingSemi-Supervised Text Classification+2ME-GCN: Multi-dimensional Edge-Enhanced Graph Convolutional Networks for Semi-supervised Text Classification
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks, including citation network analysis or …
Semi-Supervised Text Classificationtext-classificationText ClassificationALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have no…
ClassificationData AugmentationFew-Shot Text ClassificationSemi-Supervised Text Classification+3Data 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. In this paper, we present SeRe: a Sentence…
Data AugmentationSemi-Supervised Text ClassificationSentencetext-classification+1