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 801–850 of 3635 papers

TitleStatusHype
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal DataCode0
Hybrid uncertainty quantification for selective text classification in ambiguous tasks—0
Automatic Counterfactual Augmentation for Robust Text Classification Based on Word-Group Search—0
Low-Resource Cross-Lingual Adaptive Training for Nigerian PidginCode0
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning—0
Investigating Cross-Domain Behaviors of BERT in Review Understanding—0
On the Universal Adversarial Perturbations for Efficient Data-free Adversarial DetectionCode0
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion ClassificationCode1
Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models—0
Evolutionary Verbalizer Search for Prompt-based Few Shot Text ClassificationCode0
Description-Enhanced Label Embedding Contrastive Learning for Text ClassificationCode0
MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text ClassificationCode1
One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial SupportCode0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings—0
Rank-Aware Negative Training for Semi-Supervised Text ClassificationCode0
h2oGPT: Democratizing Large Language ModelsCode6
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production TasksCode1
Soft Language Clustering for Multilingual Model Pre-training—0
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili—0
Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces—0
Linear Classifier: An Often-Forgotten Baseline for Text ClassificationCode1
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of PseudonymizationCode0
Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models—0
Leveraging Language Identification to Enhance Code-Mixed Text Classification—0
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models—0
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text ClassificationCode0
Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification—0
Contrastive Bootstrapping for Label RefinementCode0
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label DescriptionsCode0
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels—0
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference TransformationCode0
Word Embeddings for Banking Industry—0
Learning Transformer ProgramsCode1
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
Analyzing Text Representations by Measuring Task Alignment—0
Efficient Shapley Values Estimation by Amortization for Text ClassificationCode1
Machine Learning Approach for Cancer Entities Association and Classification—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-TuningCode1
Multiscale Positive-Unlabeled Detection of AI-Generated TextsCode2
Mitigating Label Biases for In-context LearningCode1
A Two-Stage Decoder for Efficient ICD CodingCode0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias—0
Hierarchical Verbalizer for Few-Shot Hierarchical Text ClassificationCode1
Label Agnostic Pre-training for Zero-shot Text ClassificationCode1
Perturbation-based Self-supervised Attention for Attention Bias in Text Classification—0
EXnet: Efficient In-context Learning for Data-less Text classification—0
Estimating class separability of text embeddings with persistent homology—0
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST5-XXLAccuracy73.42—Unverified
2ST5-XLAccuracy72.84—Unverified
3ST5-LargeAccuracy72.31—Unverified
4Ada SimilarityAccuracy70.44—Unverified
5SGPT-5.8B-nliAccuracy70.14—Unverified
6ST5-BaseAccuracy69.81—Unverified
7SGPT-5.8B-msmarcoAccuracy68.13—Unverified
8MPNet-multilingualAccuracy67.91—Unverified
9GTR-XXLAccuracy67.41—Unverified
10SimCSE-BERT-supAccuracy67.32—Unverified
#ModelMetricClaimedVerifiedStatus
1Mistral-Small-24B + CAPOError15.7—Unverified
2ToWE-SGError14—Unverified
3Qwen2.5-32B + CAPOError12.93—Unverified
4Llama-3.3-70B + CAPOError11.2—Unverified
5Seq2CNN with GWS(50)Error9.64—Unverified
6Char-level CNNError9.51—Unverified
7SVDCNNError9.45—Unverified
8VDCNError8.67—Unverified
9Balanced+bi-leaf-RNNError7.9—Unverified
10CCCapsNetError7.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Seq2CNN(50)Error2.77—Unverified
2Char-level CNNError1.55—Unverified
3SWEM-concatError1.43—Unverified
4FastTextError1.4—Unverified
5VDCNError1.29—Unverified
6CCCapsNetError1.28—Unverified
7Balanced+bi-leaf-RNNError1.2—Unverified
8BERT large UDAError1.09—Unverified
9M-ACNNError1.07—Unverified
10EXAMError1—Unverified
#ModelMetricClaimedVerifiedStatus
1DeBERTaAccuracy98.45—Unverified
2C-BERT (ESGNN + BERT)Accuracy98.28—Unverified
3ESGNNAccuracy98.23—Unverified
4RoBERTaGCNAccuracy98.2—Unverified
5BERTAccuracy98.17—Unverified
6SGNNAccuracy98.09—Unverified
7ERNIE 2.0Accuracy98.04—Unverified
8DistilBERTAccuracy97.98—Unverified
9Our Model*Accuracy97.8—Unverified
10ALBERTv2Accuracy97.62—Unverified
#ModelMetricClaimedVerifiedStatus
1TM-GloveError9.96—Unverified
2byte mLSTM7Error9.6—Unverified
3DELTA (CNN)Error7.8—Unverified
4SWEM-averError7.8—Unverified
5Capsule-BError7.2—Unverified
6STM+TSED+PT+2LError7.04—Unverified
7GRU-RNN-GLOVEError7—Unverified
8MPAD-pathError6.2—Unverified
9VLAWEError5.8—Unverified
10C-LSTMError5.4—Unverified
#ModelMetricClaimedVerifiedStatus
1LinearSVM+TFIDFAccuracy93—Unverified
2RoBERTaGCNAccuracy89.5—Unverified
3SSGCAccuracy88.6—Unverified
4SGCAccuracy88.5—Unverified
5SGCNAccuracy88.5—Unverified
6RMDL (15 RDLs)Accuracy87.91—Unverified
7Sparse Tensor ClassifierAccuracy87.3—Unverified
8GraphStarAccuracy86.9—Unverified
9NABoE-fullAccuracy86.8—Unverified
10Text GCNAccuracy86.34—Unverified
#ModelMetricClaimedVerifiedStatus
1ELECTRA + ANNF199.6—Unverified
2ERNIE + ANNF199.4—Unverified
3XLNet + ANNF199.2—Unverified
4RoBERTa + ANNF198.7—Unverified
5Longformer + ANNF193.9—Unverified
6BERT + ANNF190.5—Unverified
7ALBERT + ANNF179.7—Unverified
8BERTF175—Unverified
9DistilBERTF174.4—Unverified
10LongformerF174—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTaGCNAccuracy72.8—Unverified
2Our Model*Accuracy69.4—Unverified
3SGCNAccuracy68.5—Unverified
4SGCAccuracy68.5—Unverified
5SSGCAccuracy68.5—Unverified
6Text GCNAccuracy68.36—Unverified
7GraphStarAccuracy64.2—Unverified
8ApproxRepSetAccuracy64.06—Unverified
9REL-RWMD k-NNAccuracy58.74—Unverified
10CNN+LowercasedAccuracy36.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT-ITPT-FiTAccuracy77.62—Unverified
2DRNNAccuracy76.26—Unverified
3DELTA (HAN)Accuracy75.1—Unverified
4EXAMAccuracy74.8—Unverified
5DNC+CUWAccuracy74.3—Unverified
6ULMFiT (Small data)Accuracy74.3—Unverified
7CCCapsNetAccuracy73.85—Unverified
8SWEM-concatAccuracy73.53—Unverified
9FastTextAccuracy72.3—Unverified
10Seq2CNN(50)Accuracy55.39—Unverified
#ModelMetricClaimedVerifiedStatus
1DeBERTaAccuracy90.21—Unverified
2RoBERTaGCNAccuracy89.7—Unverified
3ERNIE 2.0 (optimized)Accuracy89.53—Unverified
4RoBERTaAccuracy89.42—Unverified
5ERNIE 2.0Accuracy88.97—Unverified
6BERTAccuracy86.94—Unverified
7ALBERTv2Accuracy86.02—Unverified
8DistilBERTAccuracy85.31—Unverified
9SSGCAccuracy76.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CliReBERT (P0L3/clirebert_clirevocab_uncased)Evaluation Macro F10.65—Unverified
2ClimateBERT (climatebert/distilroberta-base-climate-f)Evaluation Macro F10.64—Unverified
3BERT (google-bert/bert-base-uncased)Evaluation Macro F10.61—Unverified
4CliSciBERT (P0L3/cliscibert_scivocab_uncased)Evaluation Macro F10.61—Unverified
5SciBERT (allenai/scibert_scivocab_cased)Evaluation Macro F10.59—Unverified
6DistilRoBERTa (distilbert/distilroberta-base)Evaluation Macro F10.58—Unverified
7SciClimateBERT (P0L3/sciclimatebert)Evaluation Macro F10.58—Unverified
8RoBERTa (FacebookAI/roberta-base)Evaluation Macro F10.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Human (Post-Rec.) (Spangher et al., 2021)macro F173.69—Unverified
2MT-Mac (Spangher et al., 2021)macro F163.46—Unverified
3MT-Mic (Spangher et al., 2021)macro F161.89—Unverified
4RL-IP/TT (Choubey et al., 2021)macro F157—Unverified
5Document LSTM + Document encoding (Choubey et al., 2020)macro F154.4—Unverified
6CRF Fine-grained (Choubey et al., 2020)macro F152.9—Unverified
7Human (Blind) (Spangher et al., 2021)macro F146.18—Unverified
8Feature-based (SVM) (Choubey et al., 2020)macro F138.3—Unverified
#ModelMetricClaimedVerifiedStatus
11-6 BertGCNAccuracy96.6—Unverified
2GraphStarAccuracy95—Unverified
3Our Model*Accuracy94.6—Unverified
4SSGCAccuracy94.5—Unverified
5SGCNAccuracy94—Unverified
6SGCAccuracy94—Unverified
7Text GCNAccuracy93.56—Unverified
8TM-GloveAccuracy89.14—Unverified