https://github.com/baidu/DDParser
공식 코드A Practical Chinese Dependency Parser Based on A Large-scale Dataset
Papers with Code 아카이브
Dependency Parsing
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- 5
- 평가표
- 16
- 보존된 평가 기록
- 54
Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between "head" words and words, which modify those heads.
Example:
root
|
| +-------dobj---------+
| | |
nsubj | | +------det-----+ | +-----nmod------+
+--+ | | | | | | |
| | | | | +-nmod-+| | | +-case-+ |
+ | + | + + || + | + | |
I prefer the morning flight through Denver
Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents (+ indicates the dependent).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bcmi220/erefdp
공식 코드https://github.com/bcmi220/ggdp
공식 코드https://github.com/bekou/ad_data
공식 코드https://github.com/bergen/edgetransformer
공식 코드https://github.com/bloomberg/emnlp20_depsrl
공식 코드https://github.com/bnmin/tdp_ranking
공식 코드Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
https://github.com/boschresearch/steps-parser
공식 코드https://github.com/boun-tabi/BoAT
공식 코드Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool
https://github.com/boun-tabi/UD_Turkish-BOUN
공식 코드Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool
https://github.com/briemadu/restart-inc-ambiguities
공식 코드https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs
공식 코드https://github.com/cambridgeltl/sw_study
공식 코드A Systematic Study of Leveraging Subword Information for Learning Word Representations
https://github.com/chenllliang/atp
공식 코드ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
https://github.com/chimran135/syntax-injected-sentiment-analysis
공식 코드A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis
https://github.com/clab/language-universal-parser
공식 코드https://github.com/clab/lstm-parser
공식 코드Transition-Based Dependency Parsing with Stack Long Short-Term Memory
https://github.com/clab/rnng
공식 코드What Do Recurrent Neural Network Grammars Learn About Syntax?
https://github.com/clovaai/spade
공식 코드Spatial Dependency Parsing for Semi-Structured Document Information Extraction
https://github.com/codedecde/smal
공식 코드On Efficiently Acquiring Annotations for Multilingual Models
https://github.com/coli-saar/am-parser
공식 코드https://github.com/daisean/hint
공식 코드Hierarchical information matters: Text classification via tree based graph neural network
https://github.com/daisean/tent
공식 코드Hierarchical information matters: Text classification via tree based graph neural network
https://github.com/danifg/BottomUp-Hierarchical-PtrNet
공식 코드Dependency Parsing with Bottom-up Hierarchical Pointer Networks
https://github.com/danifg/DiscoPointer
공식 코드https://github.com/danifg/Left2Right-Pointer-Parser
공식 코드https://github.com/danifg/Non-Local-Covington
공식 코드Non-Projective Dependency Parsing with Non-Local Transitions
https://github.com/danifg/SemanticPointer
공식 코드Transition-based Semantic Dependency Parsing with Pointer Networks
https://github.com/datquocnguyen/BioNLP
공식 코드From POS tagging to dependency parsing for biomedical event extraction
https://github.com/datquocnguyen/BioPosDep
공식 코드From POS tagging to dependency parsing for biomedical event extraction
https://github.com/datquocnguyen/jPTDP
공식 코드A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
https://github.com/deep-spin/unn
공식 코드https://github.com/deepsoftwareanalytics/domaincodebench
공식 코드https://github.com/dmitropolsky/assemblies
공식 코드Center-Embedding and Constituency in the Brain and a New Characterization of Context-Free Languages
https://github.com/egangu/high-order-joint-parsing
공식 코드https://github.com/elikip/bist-parser
공식 코드Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.