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/Aveek-Saha/Graph-Attention-Net
https://github.com/BIG-S2/keras-gnm
https://github.com/BPYap/Question-Generation
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/BUTSpeechFIT/BaySMM
https://github.com/Bhavneet1492/Anytime-Active-Learning
https://github.com/BinWang28/BERT_Sentence_Embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BinWang28/SBERT-WK-Sentence-Embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BingYu94860/HGNN-Node-Classification
https://github.com/Bingo-W/Personalisation-in-LLM
https://github.com/BirgerMoell/bertcastle
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BordiaS/layoutlm
https://github.com/BroCoLySTyLe/SQLovaReview
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/COMP6248-Reproducability-Challenge/Differentiables_FastAccurateTextClassification
https://github.com/CVxTz/music_genre_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/CZWin32768/xnlg
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
https://github.com/CaedenZ/distractionModel
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/Caesarzhang/bert-zh
https://github.com/CatherineWong/dancin_seq2seq
TextBugger: Generating Adversarial Text Against Real-world Applications
https://github.com/Charliesgithub20221030/BERT
https://github.com/Cheneng/DPCNN
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/ChingHuanChiu/sensitive
https://github.com/Chonwai/Learning_BERT
https://github.com/ClaudiaRaffaelli/Protein-subcellular-localization
https://github.com/Coda-s/BJTU_NLP_Practice
https://github.com/CodeChefVIT/VOID
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/CogComp/BenchmarkingZeroShot
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
https://github.com/Colanim/BERT_STS-B
https://github.com/ColdenChan/GAT_D
https://github.com/Coolgiserz/NLP_starter
https://github.com/CurryTang/Graph-LLM
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
https://github.com/CyberZHG/keras-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Cynwell/Text-Level-GNN
https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.