Paper Info
Title
Joint entity and relation extraction based on a hybrid neural network.
Abstract
Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities’ semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder LSTM module (BiLSTM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task.
Year
DOI
Venue
2017
10.1016/j.neucom.2016.12.075
Neurocomputing
Keywords
Field
DocType
Neural network,Information extraction,Tagging,Classification
Data mining,Contextual information,Pattern recognition,Computer science,Automatic Content Extraction,Hybrid neural network,Information extraction,Artificial intelligence,Relation classification,Artificial neural network,Machine learning,Relationship extraction
Journal
Volume
ISSN
Citations 
257
0925-2312
28
PageRank 
References 
Authors
1.10
40
7
Name
Order
Citations
PageRank
Suncong Zheng1847.61
Yuexing Hao2291.59
Dongyuan Lu3281.10
Hongyun Bao4694.32
Jiaming Xu528435.34
Hong-Wei Hao61636.29
Bo Xu724136.59