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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/yueyu1030/patron
공식 코드https://github.com/yul091/dgslow
공식 코드White-Box Multi-Objective Adversarial Attack on Dialogue Generation
https://github.com/yumeng5/CatE
공식 코드Discriminative Topic Mining via Category-Name Guided Text Embedding
https://github.com/yumeng5/JoSH
공식 코드Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
https://github.com/yumeng5/LOTClass
공식 코드Text Classification Using Label Names Only: A Language Model Self-Training Approach
https://github.com/yumeng5/WeSHClass
공식 코드https://github.com/yumeng5/WeSTClass
공식 코드https://github.com/yumeng5/topclus
공식 코드Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
https://github.com/yumoxu/detnet
공식 코드https://github.com/yuqi92/3-level-htn-mimic
공식 코드https://github.com/yuqing-zhou/causal-learning-for-robust-classifier
공식 코드Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective
https://github.com/yuqing-zhou/shortcut-learning-in-text-classification
공식 코드https://github.com/yuweisunn/federated-knowledge-alignment
공식 코드Feature Distribution Matching for Federated Domain Generalization
https://github.com/yuzhimanhua/HIMECat
공식 코드Hierarchical Metadata-Aware Document Categorization under Weak Supervision
https://github.com/yuzhimanhua/MATCH
공식 코드MATCH: Metadata-Aware Text Classification in A Large Hierarchy
https://github.com/yuzhimanhua/MetaCat
공식 코드https://github.com/yuzhimanhua/maple
공식 코드The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
https://github.com/yuzhimanhua/micol
공식 코드Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
https://github.com/yuzhimanhua/motifclass
공식 코드MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
https://github.com/yuzhimanhua/seetopic
공식 코드https://github.com/yvogao/laqda
공식 코드Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
https://github.com/yychai74/ld-vae
공식 코드Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
https://github.com/yyjmjc/temporal-emotion-localization-in-videos
공식 코드https://github.com/yzh119/BPT
공식 코드BP-Transformer: Modelling Long-Range Context via Binary Partitioning
https://github.com/yzhan238/pieclass
공식 코드https://github.com/zeinsh/Legal-Docs-Large-MLTC
공식 코드Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models
https://github.com/zekaouinoureddine/Opinion-Transformers
공식 코드https://github.com/zepingyu0512/in-context-mechanism
공식 코드https://github.com/zero-one-loss/wordcnn01
공식 코드https://github.com/zeroQiaoba/EmotiW2018
공식 코드Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
https://github.com/zerohd4869/HIN-SR
공식 코드Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
https://github.com/zexuehe/interpretable_debiasing
공식 코드Controlling Bias Exposure for Fair Interpretable Predictions
https://github.com/zhanglinfeng1997/Sentiment-Analysis-via-GCN
공식 코드Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
https://github.com/zhanglu-cst/classkg
공식 코드Weakly-supervised Text Classification Based on Keyword Graph
https://github.com/zhangrui4041/backdoor_complications
공식 코드The Ripple Effect: On Unforeseen Complications of Backdoor Attacks
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