https://github.com/bothe/EDAs
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
Papers with Code 아카이브
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/bothe/dialogue-act-recognition
공식 코드EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
https://github.com/bozheng-hit/xTune
공식 코드https://github.com/bplank/ijcnlp2017-customer-feedback
공식 코드ALL-IN-1: Short Text Classification with One Model for All Languages
https://github.com/bpro2410/seeded-poisson-factorization
공식 코드Seeded Poisson Factorization: Leveraging domain knowledge to fit topic models
https://github.com/brcsomnath/adversarial-scrubber
공식 코드Adversarial Scrubbing of Demographic Information for Text Classification
https://github.com/brucewlee/lingfeat
공식 코드Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features
https://github.com/bruthyu/melo
공식 코드MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
https://github.com/buguemar/grtc_gnns
공식 코드https://github.com/bwallace/rationale-CNN
공식 코드Rationale-Augmented Convolutional Neural Networks for Text Classification
https://github.com/by-liu/cals
공식 코드https://github.com/c-juhwan/adverb-deletion-aug
공식 코드Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
https://github.com/cair/TextUnderstandingTsetlinMachine
공식 코드https://github.com/cair/tmu
공식 코드Tsetlin Machine Embedding: Representing Words Using Logical Expressions
https://github.com/caiy0220/xproax
공식 코드XPROAX-Local explanations for text classification with progressive neighborhood approximation
https://github.com/cambridgeltl/mling_sdgms
공식 코드https://github.com/canyuchen/promptda
공식 코드PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
https://github.com/caohanch/paper_data_method_sharing
공식 코드https://github.com/caojie54/otseq2set
공식 코드https://github.com/captaine/searching-for-structure-in-unfalsifiable-claims
공식 코드https://github.com/carlomarxdk/trilemma-of-truth
공식 코드https://github.com/carolinazheng/revisiting-tglms
공식 코드https://github.com/caskcsg/TextSmoothing
공식 코드Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
https://github.com/casszhao/temporal-drift-on-explanation
공식 코드On the Impact of Temporal Concept Drift on Model Explanations
https://github.com/castorini/hedwig
공식 코드https://github.com/catalinstoean/FCGR-LR
공식 코드https://github.com/cbadenes/crosslingual-semantic-similarity
공식 코드Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies
https://github.com/cbluebenchmark/cblue
공식 코드CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
https://github.com/ccsasuke/man
공식 코드Multinomial Adversarial Networks for Multi-Domain Text Classification
https://github.com/cecilialeiqi/adversarial_text
공식 코드Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
https://github.com/cemoody/lda2vec
공식 코드Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
https://github.com/cgshpi/topic-models-and-dimensionality-reduction-benchmark
공식 코드https://github.com/chandar-lab/debiasing-using-attention-regularization
공식 코드Should We Attend More or Less? Modulating Attention for Fairness
https://github.com/chandar-lab/eat
공식 코드Should We Attend More or Less? Modulating Attention for Fairness
https://github.com/changmenseng/generative_calibration
공식 코드https://github.com/chaojiewang94/wgaae
공식 코드Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.