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
1Seq2CNN(50)Error2.77Unverified
2Char-level CNNError1.55Unverified
3SWEM-concatError1.43Unverified
4FastTextError1.4Unverified
5VDCNError1.29Unverified
6CCCapsNetError1.28Unverified
7Balanced+bi-leaf-RNNError1.2Unverified
8BERT large UDAError1.09Unverified
9M-ACNNError1.07Unverified
10EXAMError1Unverified