A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction

Yanbing Xiao, Guorong Chen (), Chongling Du, Lang Li, Yu Yuan, Jincheng Zou and Jingcheng Liu
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Yanbing Xiao: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Guorong Chen: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Chongling Du: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Lang Li: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Yu Yuan: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Jincheng Zou: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Jingcheng Liu: China Academy of Liquor Industry, Luzhou Vocational and Technical College, Luzhou 646608, China

Mathematics, 2023, vol. 11, issue 22, 1-13

Abstract: Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction. In this paper, we propose a Double-Headed Entities and Relations Prediction (DERP) framework, which divides the entity recognition process into two stages: head entity recognition and tail entity recognition, using the obtained head and tail entities as inputs. By utilizing the corresponding relation and the corresponding entity, the DERP framework further incorporates a triple prediction module to improve the accuracy and completeness of the joint relation triple extraction. We conducted experiments on two English datasets, NYT and WebNLG, and two Chinese datasets, DuIE2.0 and CMeIE-V2, and compared the English dataset experimental results with those derived from ten baseline models. The experimental results demonstrate the effectiveness of our proposed DERP framework for triple extraction.

Keywords: triple extraction; entity recognition; relation extraction; joint extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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