Papers with Code 아카이브
Part-Of-Speech Tagging
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Part-of-speech tagging (POS tagging) is the task of tagging a word in a text with its part of speech. A part of speech is a category of words with similar grammatical properties. Common English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.
Example:
| Vinken | , | 61 | years | old |
|---|---|---|---|---|
| NNP | , | CD | NNS | JJ |
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hbaflast/bert-sentiment-analysis-tensorflow
https://github.com/henry8527/GCE
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/heraclex12/VLSP2020-Fake-News-Detection
Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models
https://github.com/heraclex12/vietpunc
Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models
https://github.com/hope-yao/robust_attention_cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ilyagusev/rnnmorph
https://github.com/imrahulr/adversarial_robustness_pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/jedgusse/collaborative-authorship
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning
https://github.com/jeesu-jung/alignment_based_sequence_ensemble
https://github.com/jiachengli1995/jointie
Generalizing Natural Language Analysis through Span-relation Representations
https://github.com/jiesutd/PyTorchSeqLabel
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
https://github.com/jiesutd/Text-Attention-Heatmap-Visualization
https://github.com/jiesutd/yato
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
https://github.com/jkulhanek/lemmatag
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs
https://github.com/jokeryan/post_training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/jxz542189/dmn_plus
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/khieu/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/leuchine/S-LSTM
https://github.com/lifu-tu/ENGINE
Learning Approximate Inference Networks for Structured Prediction
https://github.com/lifu-tu/INFNET
Learning Approximate Inference Networks for Structured Prediction
https://github.com/linghduoduo/NLP
https://github.com/locuslab/convex_adversarial
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/locuslab/robust_overfitting
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/loes5307/vocaladversary2022
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/luizgh/adversarial_signatures
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/macco3k/deepstories
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/makcedward/nlpaug
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
https://github.com/matanbt/attack-tabular
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/microsoft/distance-learner
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/mikekestemont/anthem
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/modelscope/modelscope
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
https://github.com/monologg/korean-ner-pytorch
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.