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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/adlnlp/peach
공식 코드PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
https://github.com/adlnlp/stockemotions
공식 코드https://github.com/adrianchifu/FreSaDa
공식 코드FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection
https://github.com/advattack/bu-spo
공식 코드https://github.com/ahmad-taie/infodens
공식 코드INFODENS: An Open-source Framework for Learning Text Representations
https://github.com/ahmedesamy/dsgrl
공식 코드https://github.com/ahoho/SentiVAE
공식 코드Combining Sentiment Lexica with a Multi-View Variational Autoencoder
https://github.com/ahoho/kd-topic-models
공식 코드https://github.com/ahoho/topics
공식 코드Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
https://github.com/ai-secure/textguard
공식 코드TextGuard: Provable Defense against Backdoor Attacks on Text Classification
https://github.com/aida-ugent/cross-domain-HTC
공식 코드https://github.com/aielte-research/llambert
공식 코드https://github.com/aiintelligentsystems/next-level-bert
공식 코드NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
https://github.com/aim-harvard/sdoh
공식 코드Large Language Models to Identify Social Determinants of Health in Electronic Health Records
https://github.com/aimmemotion/emovit
공식 코드EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
https://github.com/airaria/TextBrewer
공식 코드TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
https://github.com/airi-institute/al_ats
공식 코드https://github.com/airi-institute/al_nlp_feasible
공식 코드https://github.com/akanenyan/distant-supervision-tweets
공식 코드https://github.com/akkarimi/aeda_nlp
공식 코드AEDA: An Easier Data Augmentation Technique for Text Classification
https://github.com/alali3030/earnings_calls_NLP
공식 코드Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls
https://github.com/albert-jin/agricultural_textual_classification_chatgpt
공식 코드ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification
https://github.com/albertkx/conal
공식 코드Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models
https://github.com/alexa/alexa-point-of-view-dataset
공식 코드Converting the Point of View of Messages Spoken to Virtual Assistants
https://github.com/alexandra-chron/siatl
공식 코드An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
https://github.com/alexandra-chron/wassa-2018
공식 코드NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
https://github.com/alibaba-damo-academy/spokennlp
공식 코드MUG: A General Meeting Understanding and Generation Benchmark
https://github.com/alibaba/AliceMind
공식 코드VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
https://github.com/alibaba/EasyNLP
공식 코드Towards Unified Prompt Tuning for Few-shot Text Classification
https://github.com/alieto/DEGARI
공식 코드https://github.com/alinlab/MASKER
공식 코드MASKER: Masked Keyword Regularization for Reliable Text Classification
https://github.com/alipay/StructuredLM_RTDT
공식 코드https://github.com/aliutkus/spe
공식 코드Relative Positional Encoding for Transformers with Linear Complexity
https://github.com/aliyun/cflue
공식 코드https://github.com/allambert/torch_itl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.