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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/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SelfAttention.py
https://github.com/ming024/FastSpeech2
https://github.com/mithunpaul08/bert_tensorflow
https://github.com/mitran27/Attention-is-all-you-Need
https://github.com/moein-shariatnia/Pix2Seq
https://github.com/monimoyd/NLPEnglishToPythonCodeUsingTransformer
https://github.com/monimoyd/NLPMachineTranslationUsingTransformer
https://github.com/moon23k/Scheduled_Sampling
https://github.com/moon23k/Transformer_Anchors
https://github.com/moon23k/Transformer_Archs
https://github.com/myamamoto555/tf-bert
https://github.com/nachiketaa/bert
https://github.com/nameoverflow/neuro-cangjie
https://github.com/napakalas/NLIMED
https://github.com/natel9178/transformer-refork
https://github.com/neburseni/NLP
https://github.com/neqkir/attention-mechanism
https://github.com/ngocphucck/BERT-questions-classification
https://github.com/niccolot/ENG-ITA_NMT
https://github.com/nicholasbao/nlp_job
https://github.com/nietao2/DeepSpeechRecognition
https://github.com/nlinc1905/dsilt-tsa
https://github.com/nmfisher/bert-modified
https://github.com/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nrc-cnrc/sockeye-multisource
https://github.com/nsfzyzz/generalization_metrics_for_nlp
https://github.com/nttcslab-nlp/strong_s2s_baseline_parser
https://github.com/nvshrao/Bidirectional-Transformer
https://github.com/okkteam/Transformer-Transducer
https://github.com/ololo123321/maestro
https://github.com/oneway3124/disaggregation-vrnn
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/open-mmlab/mmclassification
https://github.com/opennmt/ctranslate2
https://github.com/oplatek/e2end
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.