Document-level Relation Extraction
57 papers with code • 1 benchmarks • 1 datasets
Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.
Libraries
Use these libraries to find Document-level Relation Extraction models and implementations
DocRED-IE
Most implemented papers
Fine-tune Bert for DocRED with Two-step Process
Modelling relations between multiple entities has attracted increasing attention recently, and a new dataset called DocRED has been collected in order to accelerate the research on the document-level relation extraction.
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs
However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair.
Global-to-Local Neural Networks for Document-Level Relation Extraction
nju-websoft/GLRE
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EMNLP 2020
Relation extraction (RE) aims to identify the semantic relations between named entities in text.
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
wzhouad/ATLOP
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In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
Denoising Relation Extraction from Document-level Distant Supervision
Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance.
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
huiweizhou/gcgcn
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COLING 2020
In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document.
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
Hunter-DDM/cfer-document-level-RE
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In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction.
Document-Level Relation Extraction with Reconstruction
xwjim/DocRE-Rec
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In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
We present a joint model for entity-level relation extraction from documents.
Multi-view Inference for Relation Extraction with Uncertain Knowledge
Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks.
