Document-level Relation Extraction
64 papers with code • 3 benchmarks • 3 datasets
Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.
Benchmarks
These leaderboards are used to track progress in Document-level Relation Extraction
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Libraries
Use these libraries to find Document-level Relation Extraction models and implementations
Re-DocRED
Most implemented papers
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs.
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.
Double Graph Based Reasoning for Document-level Relation Extraction
DreamInvoker/GAIN
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EMNLP 2020
Document-level relation extraction aims to extract relations among entities within a document.
Discriminative Reasoning for Document-level Relation Extraction
xwjim/DRN
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Findings (ACL) 2021
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Document-level Relation Extraction as Semantic Segmentation
zjunlp/DocuNet
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Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
A sequence-to-sequence approach for document-level relation extraction
In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components.
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes
chanzuckerberg/chemdisgene
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LREC 2022
We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models.
AutoRE: Document-Level Relation Extraction with Large Language Models
Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation Extraction (RE).
