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/macco3k/deepstories
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/machine-learning-study-group/movie-sentiment-analysis
https://github.com/madhawav/MML
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/maexe/linspector-web
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
https://github.com/magic-lantern/nlp-transfer-learning
Universal Language Model Fine-tuning for Text Classification
https://github.com/mahendrathapa/graph-convolution-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/GKD
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/RA-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/makcedward/nlpaug
Character-level Convolutional Networks for Text Classification
https://github.com/maknotavailable/pytorch-pretrained-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mamamot/Russian-ULMFit
Universal Language Model Fine-tuning for Text Classification
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/manuelladron/Ambiguity-in-Computational-Creativity
https://github.com/marble0117/GNN_models_pytorch
https://github.com/marble0117/GNN_models_pytorch_geometric
https://github.com/marblet/GNN_models_pytorch_geometric
https://github.com/marblet/gat-pytorch
https://github.com/marekrei/mltagger
https://github.com/marfoq/unsupervised_translator
https://github.com/marshsh/SMH-extended
Topic Discovery in Massive Text Corpora Based on Min-Hashing
https://github.com/martinambition/IntentClassificationBenchmark
Subword Semantic Hashing for Intent Classification on Small Datasets
https://github.com/martinomensio/spacy-universal-sentence-encoder
https://github.com/masukislam/DS8008_NLP
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles
https://github.com/maximilianahrens/scholar4regression
https://github.com/maxudong1993/mybert
https://github.com/mayank-git-hub/Text-Recognition
https://github.com/mayurnewase/Quora-Bert
https://github.com/mdaniluk/language-detector
Universal Language Model Fine-tuning for Text Classification
https://github.com/meelfy/pytorch_pretrained_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/megagonlabs/rotom
https://github.com/meizi1114/bert
https://github.com/meliketoy/graph-cnn.pytorch
https://github.com/memray/OpenNMT-kpg-release
https://github.com/memray/llm_phrase_semantics
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.