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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.
https://github.com/han-shi/SparseBERT
https://github.com/hannibal046/nanorwkv
https://github.com/hantek/distance-parser
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
https://github.com/harshit158/paper-dots
https://github.com/harvardnlp/annotated-transformer
https://github.com/hfxunlp/transformer
https://github.com/hiun/learning-transformers
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
https://github.com/hrbigelow/transformer-aiayn
https://github.com/hubiodatalab/druggen
https://github.com/huggingface/transformers
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/i-machine-think/emergent_grammar_induction
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/insigh/THUMT
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jamesdvance/predicting_clicks
https://github.com/jangjoongkeon/JK
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.