Document-level Relation Extraction
40 papers with code • 0 benchmarks • 0 datasets
Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.
Benchmarks
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Libraries
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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.
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.
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.
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.
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
fenchri/edge-oriented-graph
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IJCNLP 2019
We thus propose an edge-oriented graph neural model for document-level relation extraction.
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.
