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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/lumia-group/apl
공식 코드https://github.com/lumia-group/lot-insts
공식 코드Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
https://github.com/luohongyin/langcode
공식 코드Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
https://github.com/luoxiaoheics/xal
공식 코드XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners
https://github.com/luoyuanlab/clinical-longformer
공식 코드Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
https://github.com/lushleaf/Structure-free-certified-NLP
공식 코드SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions
https://github.com/lvapeab/nmt-keras
공식 코드NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
https://github.com/lxing532/topic_variability
공식 코드https://github.com/lzhang90/prompt/blob/main/prompts
공식 코드https://github.com/lzw108/mmaffben
공식 코드MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs
https://github.com/m-hahn/sensitivity
공식 코드Sensitivity as a Complexity Measure for Sequence Classification Tasks
https://github.com/maarten-deraedt/emnlp2022-robustifying-sentiment-classification
공식 코드Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
https://github.com/maartengr/bertopic_evaluation
공식 코드BERTopic: Neural topic modeling with a class-based TF-IDF procedure
https://github.com/macarbonneau/MILSurvey
공식 코드Multiple Instance Learning: A Survey of Problem Characteristics and Applications
https://github.com/machamp-nlp/machamp
공식 코드Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
https://github.com/machine-intelligence-laboratory/OptimalNumberOfTopics
공식 코드Determination of the Number of Topics Intrinsically: Is It Possible?
https://github.com/magiccircuit/treemix
공식 코드TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
https://github.com/mahmouddaif/AraDIC
공식 코드https://github.com/mahossam/Explain2Attack
공식 코드Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
https://github.com/malteos/aspect-document-embeddings
공식 코드Specialized Document Embeddings for Aspect-based Similarity of Research Papers
https://github.com/malteos/aspect-document-similarity
공식 코드https://github.com/malteos/pytorch-bert-document-classification
공식 코드Enriching BERT with Knowledge Graph Embeddings for Document Classification
https://github.com/malteos/scincl
공식 코드Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
https://github.com/malteos/semantic-document-relations
공식 코드Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles
https://github.com/manisha-Singh-UW/LING573_HUE-Human-Understanding-and-Empathy
공식 코드https://github.com/manuelvloureiro/topics-as-entity-clusters
공식 코드Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks
https://github.com/mario-sanz/cicl
공식 코드Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models
https://github.com/markchenyutian/t5-sentinel-public
공식 코드Token Prediction as Implicit Classification to Identify LLM-Generated Text
https://github.com/markmets/immigration-prediction-est
공식 코드https://github.com/martingerlach/hSBM_Topicmodel
공식 코드https://github.com/mateuszbaransanok/trustworthyai
공식 코드Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks
https://github.com/mattbierbaum/arxiv-public-datasets
공식 코드https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/mattjin19/rbf
공식 코드https://github.com/matwerner/fast-wmd
공식 코드Speeding up Word Mover's Distance and its variants via properties of distances between embeddings
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