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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/YimiAChack/GraphSTONE
공식 코드https://github.com/YipingNUS/OptimSeed
공식 코드Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation
https://github.com/YipingNUS/contextual-eval-dataset
공식 코드Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia
https://github.com/YivanZhang/lio
공식 코드Approximating Instance-Dependent Noise via Instance-Confidence Embedding
https://github.com/YujiaBao/Distributional-Signatures
공식 코드https://github.com/YujiaBao/Predict-then-Interpolate
공식 코드Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
https://github.com/Yusufma03/pfrnns
공식 코드https://github.com/ZeroRin/BertGCN
공식 코드BertGCN: Transductive Text Classification by Combining GCN and BERT
https://github.com/ZeweiChu/NatCat
공식 코드NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
https://github.com/ZeweiChu/ULR
공식 코드Unsupervised Label Refinement Improves Dataless Text Classification
https://github.com/ZhaoRichard/nproc
공식 코드https://github.com/Zhe-Young/WICL
공식 코드https://github.com/ZhuiyiTechnology/roformer
공식 코드RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/ZhuoXu-CSE/RoBERTa-wwm-ext-Chinese-Text-Classification
공식 코드https://github.com/ZihanWangKi/XClass
공식 코드X-Class: Text Classification with Extremely Weak Supervision
https://github.com/ZizhouJia/PDANet
공식 코드PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
https://github.com/aaronmueller/contextualized-topic-models
공식 코드Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
https://github.com/abbahaddou/DGOW
공식 코드https://github.com/abdulsittar/bc-inferences-sentiments
공식 코드https://github.com/abel2code/the_butterfly_effect_of_prompts
공식 코드https://github.com/abhijitmishra/sentinellm-aaai2024
공식 코드https://github.com/abubakarsiddiqurrahman/depressionemo
공식 코드DepressionEmo: A novel dataset for multilabel classification of depression emotions
https://github.com/abuchmueller/Twitmo
공식 코드Twitmo: A Twitter Data Topic Modeling and Visualization Package for R
https://github.com/acadTags/Explainable-Automated-Medical-Coding
공식 코드https://github.com/accord-project/code-accord
공식 코드https://github.com/adalmia96/Cluster-Analysis
공식 코드Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
https://github.com/adeepH/Kan_hope
공식 코드https://github.com/adeeph/kanhope
공식 코드https://github.com/adhyasuman/ctmkd
공식 코드Improving Neural Topic Models with Wasserstein Knowledge Distillation
https://github.com/adhyasuman/ctmneg
공식 코드Improving Contextualized Topic Models with Negative Sampling
https://github.com/adhyasuman/eval_negtm
공식 코드Evaluating Negative Sampling Approaches for Neural Topic Models
https://github.com/adhyasuman/ginopic
공식 코드https://github.com/adhyasuman/ntms_dropout_analysis
공식 코드Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling
https://github.com/adjidieng/DETM
공식 코드https://github.com/adjidieng/ETM
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.