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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/EuphoriaYan/bert_component
https://github.com/Fadeich/HotFlip-CNN-pytorch
Character-level Convolutional Networks for Text Classification
https://github.com/Farahn/AES
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/FardinRastakhiz/CNN-GNN-TEXT
https://github.com/FaskyCC/TextClassification
https://github.com/FelixHub/custom-LSEP-loss-function
ML-Net: multi-label classification of biomedical texts with deep neural networks
https://github.com/FengJiaChunFromSYSU/Bert
https://github.com/FengJiaChunFromSYSU/fastText
https://github.com/FengJiaChunFromSYSU/ntua-slp-semeval2018
https://github.com/FoxerLee/TGCN
https://github.com/Fred-Chung/UBD
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/GCaptainNemo/optimization-project
https://github.com/GT-SALT/AAAI_CLF
Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
https://github.com/Gaozhen0816/BERT_QA_For_AILaw
https://github.com/Gaozhen0816/BERT_QA_for_Chinese
https://github.com/GatorSense/PMLDA
https://github.com/GauthierDmn/question_answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/GilesStrong/SuperTML_HiggsML_Test
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/GitF82/NLP-Embeddings-Protoformer-Paper
https://github.com/GitHubSprint/fasttext4j
https://github.com/GraphSAINT/GraphSAINT
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/Guowei-Yan/Data
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HIT-SCIR/ELMoForManyLangs
https://github.com/HJHGJGHHG/Paddle-FNet
https://github.com/HamzaGbada/GCN-Numpy
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HanjieChen/HEDGE
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
https://github.com/HanjieChen/VMASK
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/Helsinki-NLP/OpenNMT-py
https://github.com/HeoTaksung/MIMIC-III_CNN_Attention
An Explainable CNN Approach for Medical Codes Prediction from Clinical Text
https://github.com/HeoTaksung/Very-Deep-Convolutional-Networks-for-Text-Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.