Text Classification
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Text Classification Algorithms: A Survey )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alexandra-chron/ntua-slp-semeval2018
https://github.com/alexandra-chron/ntua-slp-wassa-iest2018
Universal Language Model Fine-tuning for Text Classification
https://github.com/alexismailov2/FastDTM
https://github.com/algharak/BERTenhance
https://github.com/algoashutosh/sentiment-analysis-project
Universal Language Model Fine-tuning for Text Classification
https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
https://github.com/alibaba/megatron-llama
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/alikhodadoost/Persian-Sentence-Augmenter
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/aliyun/qwen-dianjin
https://github.com/allenai/aspire
SPECTER: Document-level Representation Learning using Citation-informed Transformers
https://github.com/allenai/bilm-tf
https://github.com/allenhaozhu/SSGC
https://github.com/alonjacovi/interpreting-cnn-for-text
Understanding Convolutional Neural Networks for Text Classification
https://github.com/alrope123/prompt-waywardness
Character-level Convolutional Networks for Text Classification
https://github.com/alvinchangw/COCON_ICLR2021
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
https://github.com/amagooda/SummaRuNNer_coattention
Universal Language Model Fine-tuning for Text Classification
https://github.com/amaiya/causalnlp
https://github.com/amauriciorr/AubreyBot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ambityga/memsem
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amoramine/FNet_classification
https://github.com/amoramine/FNet_with_BART_classification
https://github.com/amymariaparker2401/new
https://github.com/andi611/Mockingjay-Speech-Representation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/andrewfwalters/w266-final-project
https://github.com/andyweizhao/NLP-Capsule
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/andyweizhao/capsule
Investigating Capsule Networks with Dynamic Routing for Text Classification
https://github.com/aniketfadia96/Convolutional-Neural-Networks-for-Text-Classification
https://github.com/anish-lu-yihe/SVRT-by-GAT
https://github.com/ankurbanga/Language-Models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/anonwhymoos/connectivity
https://github.com/anouck96/parsingfrisian
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.