Joint entity and relation extraction based on a hybrid neural network

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 c...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 257; pp. 59 - 66
Main Authors Zheng, Suncong, Hao, Yuexing, Lu, Dongyuan, Bao, Hongyun, Xu, Jiaming, Hao, Hongwei, Xu, Bo
Format Journal Article
LanguageEnglish
Published Elsevier B.V 27.09.2017
Subjects
Classification
Information extraction
Neural network
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Tagging
Information extraction
Neural network
Classification
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.12.075