Relationship Extraction (Distant Supervised)

9 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).

Most implemented papers

Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention

malllabiisc/RESIDE 19 Apr 2018

Relation extraction is the problem of classifying the relationship between two entities in a given sentence.

Joint Bootstrapping Machines for High Confidence Relation Extraction

pgcool/Joint-Bootstrapping-Machines NAACL 2018

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 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 18 Sep 2020

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

lingyongyan/docds 8 Dec 2020

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

DespinaChristou/REDSandT 1 Feb 2021

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 24 May 2021

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 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).