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Document-level Relation Extraction
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Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
Document-Level Relation Extraction with Reconstruction
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
Denoising Relation Extraction from Document-level Distant Supervision
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Double Graph Based Reasoning for Document-level Relation Extraction
Global-to-Local Neural Networks for Document-Level Relation Extraction
Entity and Evidence Guided Relation Extraction for DocRED
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
Fine-tune Bert for DocRED with Two-step Process
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.