https://github.com/navodhya/DMN
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Papers with Code 아카이브
Part-Of-Speech Tagging
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 15
- 보존된 평가 기록
- 48
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/ndb796/pytorch-adversarial-training-cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/openai/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/oroszgy/spacy-hungarian-models
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
https://github.com/peck94/cann-detector
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/phsfr/UD_Persian-PerDT
https://github.com/prithviraj-maurya/alexa-point-of-view-dataset
Converting the Point of View of Messages Spoken to Virtual Assistants
https://github.com/pth1993/NNVLP
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit
https://github.com/pwc-1/Paper-8/tree/main/camembert
https://github.com/qGentry/MetaBiLSTM
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
https://github.com/qanastek/ANTILLES
https://github.com/revbucket/mister_ed
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/rezkaaufar/pytorch-cvt
https://github.com/rgsachin/DMTN
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/riedlma/sequence_tagging
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
https://github.com/salomonhotegni/MOREL
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/samueljamesbell/sequence-labeler
https://github.com/sarthakTUM/progressive-neural-networks-for-nlp
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
https://github.com/sb-b/steps-parser
https://github.com/scakc/QAwiki
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/scenarri/s2m-tea
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling
Empower Sequence Labeling with Task-Aware Neural Language Model
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/soujanyaporia/aspect-extraction
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
https://github.com/tensorflow/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/syntaxnet
https://github.com/thomashopkins32/PGDAdversarialLearning
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/tianzheng4/Distributionally-Adversarial-Attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/timerstime/SDG4DA
https://github.com/tyliupku/Arbitrary-Order-Infnet
Learning Approximate Inference Networks for Structured Prediction
https://github.com/uahmad235/NER-Deep-Learning
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
https://github.com/ucsb-nlp-chang/textgrad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ukplab/acl2022-impli
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
https://github.com/val-iisc/flss
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/vchudinov/dynamic_memory_networks_with_keras
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.