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Constituency Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Constituency parsing aims to extract a constituency-based parse tree from a sentence that represents its syntactic structure according to a phrase structure grammar.
Example:
Sentence (S)
|
+-------------+------------+
| |
Noun (N) Verb Phrase (VP)
| |
John +-------+--------+
| |
Verb (V) Noun (N)
| |
sees Bill
Recent approaches convert the parse tree into a sequence following a depth-first traversal in order to be able to apply sequence-to-sequence models to it. The linearized version of the above parse tree looks as follows: (S (N) (VP V N)).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/akashe/Python-Code-Generation
https://github.com/algharak/BERTenhance
https://github.com/alibaba-damo-academy/FunASR
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/anaezquerro/incpar
Strongly Incremental Constituency Parsing with Graph Neural Networks
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu293/transformer_from_scratch
https://github.com/anirudharc/transformer
https://github.com/anishnarkar/experiments_deeplearning_models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/antoinecollas/transformer_neural_machine_translation
https://github.com/anton-bushuiev/ppiformer
https://github.com/anuscode/nrms
https://github.com/appcoreopc/berty
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/asadovsky/nn
https://github.com/asfathermou/human-computer-interaction
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/atpaino/deep-text-corrector
https://github.com/augustgw/early-exit-transformer
https://github.com/autobotasia/vibert
https://github.com/awslabs/sockeye
https://github.com/ays-dev/keras-transformer
https://github.com/bangoc123/transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/bcmi220/esc4nmt
https://github.com/bdusell/stack-attention
https://github.com/benjamintaiwo/Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.