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/nicolashernandez/readi-lrec22
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nicolay-r/RuSentRel-Leaderboard
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?
https://github.com/nieci2024/pyglcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/nikitajat/Semi-Supervised-Text-Classfication/blob/main/delta_training.ipynb
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings
https://github.com/ninoxjy/graph-embedding
https://github.com/nishee0521/Sarcasm-Detector
Universal Language Model Fine-tuning for Text Classification
https://github.com/nithishkaviyan/Sentiment-Analysis-of-Yelp-Reviews
https://github.com/nlp-research/bilm-tf
https://github.com/nlp-rise/climateeval
Towards Answering Climate Questionnaires from Unstructured Climate Reports
https://github.com/nlp-tlp/mwo2kg-and-echidna
https://github.com/nlpAThits/WOMBAT
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
https://github.com/nmfisher/bert-modified
https://github.com/nnaakkaaii/g2-MLP
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2
https://github.com/noelkelias/multitok
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/noise-field/Russian-ULMFit
Universal Language Model Fine-tuning for Text Classification
https://github.com/nuke705/DeepLearning
https://github.com/nuke705/VDCNN
https://github.com/nvshrao/Pytorch-GLUE
https://github.com/nyk510/scdv-python
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
https://github.com/oarriaga/paz
https://github.com/obi-ml-public/ehr_deidentification
https://github.com/obj2vec/obj2vec
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
https://github.com/octanove/grammartagger
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/octanove/shiba
https://github.com/oj9040/GraphSAGE_RL
https://github.com/okanvk/ENELPI---Soru-Cevap-Sistemi
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/oleksost/Stochastic_VI_LDA
https://github.com/om00839/machine-suneung
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/omarfoq/unsupervised_translator
https://github.com/ondrejba/tf_calibrate
https://github.com/oneflow-inc/libai
https://github.com/ongunuzaymacar/comparatively-finetuning-bert
https://github.com/opconty/pixellink_keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.