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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/paarthneekhara/rnn_adversarial_reprogramming
공식 코드Adversarial Reprogramming of Text Classification Neural Networks
https://github.com/pai-smallisallyourneed/mixup-amp
공식 코드Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup
https://github.com/pallgeuer/novic
공식 코드https://github.com/pallgeuer/object_noun_dictionary
공식 코드https://github.com/panagiotisanagnostou/ai-ga
공식 코드https://github.com/patricia-pereira/deep-emotion-recognition-in-textual-conversations
공식 코드https://github.com/patrickjae/GeneralizedDynamicTopicModels.jl
공식 코드https://github.com/paul-rottger/temporal-adaptation
공식 코드https://github.com/pavel-blinov/RuMedBench
공식 코드RuMedBench: A Russian Medical Language Understanding Benchmark
https://github.com/pedrada88/preproc-textclassification
공식 코드https://github.com/peiranli/ARYA
공식 코드https://github.com/perathambkk/case_shared_task_emnlp2022
공식 코드https://github.com/perathambkk/euphemism_shared_task_emnlp2022
공식 코드TEDB System Description to a Shared Task on Euphemism Detection 2022
https://github.com/persona-bench/persona
공식 코드A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
https://github.com/peterbhase/ExplanationSearch
공식 코드https://github.com/peterlau0626/semeval2023-task4-humanvalue
공식 코드https://github.com/pfnet-research/contextual_augmentation
공식 코드Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
https://github.com/pgcool/iDocNADE
공식 코드https://github.com/pgcool/iDocNADEe
공식 코드Document Informed Neural Autoregressive Topic Models with Distributional Prior
https://github.com/pgcool/textTOvec
공식 코드https://github.com/phanchauthang/ViCGCN
공식 코드https://github.com/phd-lanyu/spikegraphormer
공식 코드SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
https://github.com/philipph77/DAPE-Framework
공식 코드Domain-Invariant Representation Learning from EEG with Private Encoders
https://github.com/philipph77/acse-framework
공식 코드Exploiting Multiple EEG Data Domains with Adversarial Learning
https://github.com/phosseini/GisPy
공식 코드https://github.com/phosseini/LexiPers
공식 코드https://github.com/pieter-jacobs/bachelor-thesis
공식 코드Active learning for reducing labeling effort in text classification tasks
https://github.com/pilancilab/Randomized-Geometric-Algebra-Methods-for-Convex-Neural-Networks
공식 코드Randomized Geometric Algebra Methods for Convex Neural Networks
https://github.com/piotrmp/bodega
공식 코드Verifying the Robustness of Automatic Credibility Assessment
https://github.com/pkargupta/MEGClass
공식 코드MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities
https://github.com/pkseeg/df1
공식 코드https://github.com/pkseeg/tte_depth
공식 코드Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings
https://github.com/plkumjorn/CNNAnalysis
공식 코드Human-grounded Evaluations of Explanation Methods for Text Classification
https://github.com/pluslabnlp/cdm
공식 코드Open-Domain Text Evaluation via Contrastive Distribution Methods
https://github.com/pluslabnlp/deam
공식 코드DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.