Relationship Extraction (Distant Supervised)
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Benchmarks
New York Times Corpus
NYT
Most implemented
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
Papers
Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Environment, Social, and Governance (ESG) KPIs assess an organization's performance on issues such as climate change, greenhouse gas emissions, water consumption, waste management, human rights, diversity, and policies. …
Information ExtractionNamed Entity RecognitionOpen Information ExtractionRelationship Extraction (Distant Supervised)+1KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting,…
Graph Neural NetworkRelationRelation ExtractionRelationship Extraction (Distant Supervised)+1Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tail…
DenoisingRelationRelation ExtractionRelationship Extraction (Distant Supervised)+1Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings
Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi-instance learning and by providing…
RelationRelation ExtractionRelationship Extraction (Distant Supervised)From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
Distant supervision (DS) is a promising approach for relation extraction but often suffers from the noisy label problem. Traditional DS methods usually represent an entity pair as a bag of sentences and denoise labels us…
DenoisingMachine Reading ComprehensionReading ComprehensionRelation+3RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to lear…
Graph Neural NetworkRelationRelation ExtractionRelationship Extraction (Distant Supervised)+1