Relationship Extraction (Distant Supervised)
10 papers with code • 2 benchmarks • 1 datasets
Relationship extraction is the task of extracting semantic relationships from a text. Extracted relationships usually occur between two or more entities of a certain type (e.g. Person, Organisation, Location) and fall into a number of semantic categories (e.g. married to, employed by, lives in).
New York Times Annotated Corpus
Most implemented papers
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Relation extraction is the problem of classifying the relationship between two entities in a given sentence.
Joint Bootstrapping Machines for High Confidence Relation Extraction
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances.
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
malllabiisc/RESIDE
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EMNLP 2018
In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
ansonb/RECON
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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).
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
By re-organizing all sentences about an entity as a document and extracting relations via querying the document with relation-specific questions, the document-based DS paradigm can simultaneously encode and exploit all sentence-level, inter-sentence-level, and entity-level evidence.
Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings
We propose REDSandT (Relation Extraction with Distant Supervision and Transformers), a novel distantly-supervised transformer-based RE method, that manages to capture a wider set of relations through highly informative instance and label embeddings for RE, by exploiting BERT's pre-trained model, and the relationship between labels and entities, respectively.
Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
tmliang/CGRE
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On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously.
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
nadgeri14/KGPool
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Findings (ACL) 2021
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).
