SOTAVerified

Text Classification

Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.

In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.

( Image credit: Text Classification Algorithms: A Survey )

Papers

Showing 110 of 3635 papers

TitleStatusHype
Making Language Model a Hierarchical Classifier and GeneratorCode0
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text RepresentationCode0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackCode0
The Trilemma of Truth in Large Language ModelsCode0
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems0
Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?0
SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping0
Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages0
mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks0
MultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RoBERTaGCNAccuracy72.8Unverified
2Our Model*Accuracy69.4Unverified
3SSGCAccuracy68.5Unverified
4SGCNAccuracy68.5Unverified
5SGCAccuracy68.5Unverified
6Text GCNAccuracy68.36Unverified
7GraphStarAccuracy64.2Unverified
8ApproxRepSetAccuracy64.06Unverified
9REL-RWMD k-NNAccuracy58.74Unverified
10CNN+LowercasedAccuracy36.2Unverified