Papers with Code 아카이브
Constituency Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 4
- 보존된 평가 기록
- 40
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/feizc/Machine-Translation-Pytorch
https://github.com/felixSchober/ABSA-Transformer
https://github.com/fjiangai/mmapis
https://github.com/flxst/nerblackbox
https://github.com/flyliu2017/bert
https://github.com/flyliu2017/bert_modularized
https://github.com/fra-mari/The-Karl-Marx-Press-Review
https://github.com/frankcgq105/BERTCHEN
https://github.com/franknb/Self-attention-DCGAN
https://github.com/franknb/Text-Summarization
https://github.com/fstahlberg/tensor2tensor
https://github.com/galsang/galsang
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models
https://github.com/gavincaoyuji/NLP
https://github.com/gggggeun/DeepLearning_Architecture
https://github.com/giagia96/DeepComedy
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmum/dl-mo-2021
https://github.com/gofortargets/rnng
https://github.com/going-doer/paper2code
https://github.com/goldenbili/Bert_Test2
https://github.com/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/gongshuangshuang/deep-text-corrector
https://github.com/goodluck4s/bert-
https://github.com/google/flax/tree/master/examples/wmt
https://github.com/gooofy/zbrain
https://github.com/gooppe/transformer-summarization
https://github.com/gordicaleksa/pytorch-original-transformer
https://github.com/graykode/gpt-2-Pytorch
https://github.com/graykode/nlp-tutorial
https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network
https://github.com/guoyaohua/BERT-Chinese-Annotation
https://github.com/guzhang480/Google_BERT
https://github.com/h4ste/oscar
https://github.com/haidershaour/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.