Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence

by   Seongsik Park, et al.

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations using a forward object decoder. Then, it finds 1-to-n subject-object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8 and an F1-score of 78.3


page 1

page 2

page 3

page 4


Improving Sentence-Level Relation Extraction through Curriculum Learning

Sentence-level relation extraction mainly aims to classify the relation ...

Effective Attention Modeling for Neural Relation Extraction

Relation extraction is the task of determining the relation between two ...

Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction

A relation tuple consists of two entities and the relation between them,...

Unsupervised Open Relation Extraction

We explore methods to extract relations between named entities from free...

Ensemble Neural Relation Extraction with Adaptive Boosting

Relation extraction has been widely studied to extract new relational fa...

A Hybrid Model of Classification and Generation for Spatial Relation Extraction

Extracting spatial relations from texts is a fundamental task for natura...

Building a PubMed knowledge graph

PubMed is an essential resource for the medical domain, but useful conce...