https://github.com/louchao98/nner_as_parsing
공식 코드Nested Named Entity Recognition as Latent Lexicalized 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)).
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing
Unlexicalized Transition-based Discontinuous Constituency Parsing
Sequence Labeling Parsing by Learning Across Representations
Automatic Extraction of Clausal Embedding Based on Large-Scale English Text Data
Generalizing Natural Language Analysis through Span-relation Representations
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Grammar Induction with Neural Language Models: An Unusual Replication
Strongly Incremental Constituency Parsing with Graph Neural Networks
Investigating Non-local Features for Neural Constituency Parsing
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences
To be Continuous, or to be Discrete, Those are Bits of Questions
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars
Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.