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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/billptw/fasttrees
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https://github.com/bkoch4142/attention-is-all-you-need-paper
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https://github.com/brainsqueeze/text2vec
https://github.com/brandontrabucco/scaled_dot_product_attention
https://github.com/brightmart/bert_customized
https://github.com/brightmart/bert_original
https://github.com/bshantam97/Attention_Based_Networks
https://github.com/bytedance/neurst
https://github.com/bzhangGo/zero
https://github.com/cadia-lvl/fastspeech2
https://github.com/cahya-wirawan/indonesian-language-models
https://github.com/camlaedtke/NLP
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https://github.com/caoyujiALgLM/NLP-progress
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https://github.com/conceptofmind/lamda-rlhf-pytorch
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/seq2seq
https://github.com/cospplay/bert-master
https://github.com/coxy1989/tfmr
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.