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 26762700 of 3635 papers

TitleStatusHype
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey0
Low-resource Personal Attribute Prediction from Conversation0
Low-Resource Text Classification using Domain-Adversarial Learning0
Low-Resource Text Classification via Cross-lingual Language Model Fine-tuning0
Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches0
LSHTC: A Benchmark for Large-Scale Text Classification0
LST: Lexicon-Guided Self-Training for Few-Shot Text Classification0
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles0
LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding0
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language0
Machine-guided Solution to Mathematical Word Problems0
Machine Learning Approach for Cancer Entities Association and Classification0
Machine learning approach for text and document mining0
Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research0
Machines Do See Color: A Guideline to Classify Different Forms of Racist Discourse in Large Corpora0
MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification0
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning0
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets0
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking0
Making Sense of Hidden Layer Information in Deep Networks by Learning Hierarchical Targets0
Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA0
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets0
Semantics Preserving Adversarial Learning0
Show:102550
← PrevPage 108 of 146Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST5-XXLAccuracy73.42Unverified
2ST5-XLAccuracy72.84Unverified
3ST5-LargeAccuracy72.31Unverified
4Ada SimilarityAccuracy70.44Unverified
5SGPT-5.8B-nliAccuracy70.14Unverified
6ST5-BaseAccuracy69.81Unverified
7SGPT-5.8B-msmarcoAccuracy68.13Unverified
8MPNet-multilingualAccuracy67.91Unverified
9GTR-XXLAccuracy67.41Unverified
10SimCSE-BERT-supAccuracy67.32Unverified
#ModelMetricClaimedVerifiedStatus
1Mistral-Small-24B + CAPOError15.7Unverified
2ToWE-SGError14Unverified
3Qwen2.5-32B + CAPOError12.93Unverified
4Llama-3.3-70B + CAPOError11.2Unverified
5Seq2CNN with GWS(50)Error9.64Unverified
6Char-level CNNError9.51Unverified
7SVDCNNError9.45Unverified
8VDCNError8.67Unverified
9Balanced+bi-leaf-RNNError7.9Unverified
10CCCapsNetError7.61Unverified
#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
#ModelMetricClaimedVerifiedStatus
1DeBERTaAccuracy98.45Unverified
2C-BERT (ESGNN + BERT)Accuracy98.28Unverified
3ESGNNAccuracy98.23Unverified
4RoBERTaGCNAccuracy98.2Unverified
5BERTAccuracy98.17Unverified
6SGNNAccuracy98.09Unverified
7ERNIE 2.0Accuracy98.04Unverified
8DistilBERTAccuracy97.98Unverified
9Our Model*Accuracy97.8Unverified
10ALBERTv2Accuracy97.62Unverified
#ModelMetricClaimedVerifiedStatus
1TM-GloveError9.96Unverified
2byte mLSTM7Error9.6Unverified
3SWEM-averError7.8Unverified
4DELTA (CNN)Error7.8Unverified
5Capsule-BError7.2Unverified
6STM+TSED+PT+2LError7.04Unverified
7GRU-RNN-GLOVEError7Unverified
8MPAD-pathError6.2Unverified
9VLAWEError5.8Unverified
10C-LSTMError5.4Unverified
#ModelMetricClaimedVerifiedStatus
1LinearSVM+TFIDFAccuracy93Unverified
2RoBERTaGCNAccuracy89.5Unverified
3SSGCAccuracy88.6Unverified
4SGCAccuracy88.5Unverified
5SGCNAccuracy88.5Unverified
6RMDL (15 RDLs)Accuracy87.91Unverified
7Sparse Tensor ClassifierAccuracy87.3Unverified
8GraphStarAccuracy86.9Unverified
9NABoE-fullAccuracy86.8Unverified
10Text GCNAccuracy86.34Unverified
#ModelMetricClaimedVerifiedStatus
1ELECTRA + ANNF199.6Unverified
2ERNIE + ANNF199.4Unverified
3XLNet + ANNF199.2Unverified
4RoBERTa + ANNF198.7Unverified
5Longformer + ANNF193.9Unverified
6BERT + ANNF190.5Unverified
7ALBERT + ANNF179.7Unverified
8BERTF175Unverified
9DistilBERTF174.4Unverified
10XLNetF174Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTaGCNAccuracy72.8Unverified
2Our Model*Accuracy69.4Unverified
3SSGCAccuracy68.5Unverified
4SGCAccuracy68.5Unverified
5SGCNAccuracy68.5Unverified
6Text GCNAccuracy68.36Unverified
7GraphStarAccuracy64.2Unverified
8ApproxRepSetAccuracy64.06Unverified
9REL-RWMD k-NNAccuracy58.74Unverified
10CNN+LowercasedAccuracy36.2Unverified
#ModelMetricClaimedVerifiedStatus
1BERT-ITPT-FiTAccuracy77.62Unverified
2DRNNAccuracy76.26Unverified
3DELTA (HAN)Accuracy75.1Unverified
4EXAMAccuracy74.8Unverified
5DNC+CUWAccuracy74.3Unverified
6ULMFiT (Small data)Accuracy74.3Unverified
7CCCapsNetAccuracy73.85Unverified
8SWEM-concatAccuracy73.53Unverified
9FastTextAccuracy72.3Unverified
10Seq2CNN(50)Accuracy55.39Unverified
#ModelMetricClaimedVerifiedStatus
1DeBERTaAccuracy90.21Unverified
2RoBERTaGCNAccuracy89.7Unverified
3ERNIE 2.0 (optimized)Accuracy89.53Unverified
4RoBERTaAccuracy89.42Unverified
5ERNIE 2.0Accuracy88.97Unverified
6BERTAccuracy86.94Unverified
7ALBERTv2Accuracy86.02Unverified
8DistilBERTAccuracy85.31Unverified
9SSGCAccuracy76.7Unverified
#ModelMetricClaimedVerifiedStatus
1CliReBERT (P0L3/clirebert_clirevocab_uncased)Evaluation Macro F10.65Unverified
2ClimateBERT (climatebert/distilroberta-base-climate-f)Evaluation Macro F10.64Unverified
3BERT (google-bert/bert-base-uncased)Evaluation Macro F10.61Unverified
4CliSciBERT (P0L3/cliscibert_scivocab_uncased)Evaluation Macro F10.61Unverified
5SciBERT (allenai/scibert_scivocab_cased)Evaluation Macro F10.59Unverified
6DistilRoBERTa (distilbert/distilroberta-base)Evaluation Macro F10.58Unverified
7SciClimateBERT (P0L3/sciclimatebert)Evaluation Macro F10.58Unverified
8RoBERTa (FacebookAI/roberta-base)Evaluation Macro F10.57Unverified
#ModelMetricClaimedVerifiedStatus
1Human (Post-Rec.) (Spangher et al., 2021)macro F173.69Unverified
2MT-Mac (Spangher et al., 2021)macro F163.46Unverified
3MT-Mic (Spangher et al., 2021)macro F161.89Unverified
4RL-IP/TT (Choubey et al., 2021)macro F157Unverified
5Document LSTM + Document encoding (Choubey et al., 2020)macro F154.4Unverified
6CRF Fine-grained (Choubey et al., 2020)macro F152.9Unverified
7Human (Blind) (Spangher et al., 2021)macro F146.18Unverified
8Feature-based (SVM) (Choubey et al., 2020)macro F138.3Unverified
#ModelMetricClaimedVerifiedStatus
11-6 BertGCNAccuracy96.6Unverified
2GraphStarAccuracy95Unverified
3Our Model*Accuracy94.6Unverified
4SSGCAccuracy94.5Unverified
5SGCAccuracy94Unverified
6SGCNAccuracy94Unverified
7Text GCNAccuracy93.56Unverified
8TM-GloveAccuracy89.14Unverified