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Constituency Parsing
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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.
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https://github.com/rupakdas18/Research-Project-UMD-CS-5642
https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
https://github.com/rupakdas18/SuperGlue-tasks-using-BERT
https://github.com/s9891326/Fine-Tuning-BERT
https://github.com/sajadalipour7/persian-grapheme-to-phoneme-with-transformer
https://github.com/sambit9238/deep_text_corrector
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
https://github.com/sarthaxxxxx/Attention-is-all-you-need
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https://github.com/saurabhkulkarni77/BERT_multilabel
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https://github.com/shuaishuaij/Machine-Translation
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https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.