A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction
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 Predictio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/22/4583 |
| _version_ | 1797458540797886464 |
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| author | Yanbing Xiao Guorong Chen Chongling Du Lang Li Yu Yuan Jincheng Zou Jingcheng Liu |
| author_facet | Yanbing Xiao Guorong Chen Chongling Du Lang Li Yu Yuan Jincheng Zou Jingcheng Liu |
| author_sort | Yanbing Xiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:38:36Z |
| format | Article |
| id | doaj.art-d1e5f72a6c3348219bb20e0b21200fe6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:38:36Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-d1e5f72a6c3348219bb20e0b21200fe62023-11-24T14:54:05ZengMDPI AGMathematics2227-73902023-11-011122458310.3390/math11224583A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple ExtractionYanbing Xiao0Guorong Chen1Chongling Du2Lang Li3Yu Yuan4Jincheng Zou5Jingcheng Liu6Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaChina Academy of Liquor Industry, Luzhou Vocational and Technical College, Luzhou 646608, ChinaRelational 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.https://www.mdpi.com/2227-7390/11/22/4583triple extractionentity recognitionrelation extractionjoint extraction |
| spellingShingle | Yanbing Xiao Guorong Chen Chongling Du Lang Li Yu Yuan Jincheng Zou Jingcheng Liu A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction Mathematics triple extraction entity recognition relation extraction joint extraction |
| title | A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction |
| title_full | A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction |
| title_fullStr | A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction |
| title_full_unstemmed | A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction |
| title_short | A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction |
| title_sort | study on double headed entities and relations prediction framework for joint triple extraction |
| topic | triple extraction entity recognition relation extraction joint extraction |
| url | https://www.mdpi.com/2227-7390/11/22/4583 |
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