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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/hasanhuz/SpanEmo
공식 코드SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
https://github.com/havelhakimi/TLA
공식 코드Modeling Text-Label Alignment for Hierarchical Text Classification
https://github.com/hawkeoni/Semeval2020_task11
공식 코드https://github.com/hazdzz/Converter
공식 코드https://github.com/hccngu/MLADA
공식 코드Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
https://github.com/hccngu/meta-sn
공식 코드Meta-Learning Siamese Network for Few-Shot Text Classification
https://github.com/heimingx/pcm
공식 코드Progressive Class Semantic Matching for Semi-supervised Text Classification
https://github.com/hemile/iswc-challenge
공식 코드Prompting as Probing: Using Language Models for Knowledge Base Construction
https://github.com/hendraet/handwriting-classification
공식 코드Handwriting Classification for the Analysis of Art-Historical Documents
https://github.com/henrymoss/COLING2018
공식 코드Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
https://github.com/hermannkroll/supervisedtextprocessing
공식 코드https://github.com/hikaru-nara/dask
공식 코드Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
https://github.com/hila-chefer/Transformer-Explainability
공식 코드https://github.com/hiroki11x/optimizer_comparison_ood
공식 코드Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
https://github.com/hitz-zentroa/this-is-not-a-dataset
공식 코드This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models
https://github.com/hiyouga/dual-contrastive-learning
공식 코드Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
https://github.com/hjq310/text-classification-in-memristorsnn
공식 코드Text Classification in Memristor-based Spiking Neural Networks
https://github.com/hkunlp/progen
공식 코드ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
https://github.com/hkust-knowcomp/discoprompt
공식 코드DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition
https://github.com/hkust-knowcomp/wddc
공식 코드Weakly Supervised Text Classification using Supervision Signals from a Language Model
https://github.com/hlr/misinformation-detection
공식 코드Using Persuasive Writing Strategies to Explain and Detect Health Misinformation
https://github.com/hongyuanluke/pcc
공식 코드PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
https://github.com/hooshvare/parsbert
공식 코드ParsBERT: Transformer-based Model for Persian Language Understanding
https://github.com/hpicgs/topic-models-and-dimensionality-reduction-sensitivity-study
공식 코드https://github.com/hqa-attack/hqaattack-demo
공식 코드HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
https://github.com/hrouzegar/enhancing-text-classification-through-llm-driven-active-learning-and-human-annotation
공식 코드Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation
https://github.com/hsoleimani/PTM
공식 코드https://github.com/hsuvas/hoaxpedia_dataset
공식 코드https://github.com/huawei-noah/Efficient-Computing
공식 코드Multiscale Positive-Unlabeled Detection of AI-Generated Texts
https://github.com/huawei-noah/KD-NLP/tree/main/Minimax-kNN
공식 코드Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax
https://github.com/huawei-noah/noah-research
공식 코드https://github.com/huawei-noah/noah-research/tree/master/xero_align
공식 코드https://github.com/huaxiuyao/KGML
공식 코드Knowledge-Aware Meta-learning for Low-Resource Text Classification
https://github.com/huggingface/autotrain-advanced
공식 코드https://github.com/huggingface/setfit
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.