An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus.
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| Title: | An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus. |
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| Authors: | Lyu, Pin1 (AUTHOR), Yue, Yongyong1 (AUTHOR), Yu, Wengbing1 (AUTHOR), Xiao, Liqiao2 (AUTHOR), Liu, Chao3 (AUTHOR), Zheng, Pai2 (AUTHOR) pai.zheng@polyu.edu.hk |
| Source: | Journal of Engineering Design. Nov2025, Vol. 36 Issue 11, p1853-1878. 26p. |
| Subjects: | Engineering equipment, Artificial neural networks, Data mining, Corpora, Knowledge graphs |
| Abstract: | Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Design is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189105418 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lyu%2C+Pin%22">Lyu, Pin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yue%2C+Yongyong%22">Yue, Yongyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Wengbing%22">Yu, Wengbing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Liqiao%22">Xiao, Liqiao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chao%22">Liu, Chao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Pai%22">Zheng, Pai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> pai.zheng@polyu.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Design%22">Journal of Engineering Design</searchLink>. Nov2025, Vol. 36 Issue 11, p1853-1878. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Engineering+equipment%22">Engineering equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Corpora%22">Corpora</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Design is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/09544828.2024.2340392 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1853 Subjects: – SubjectFull: Engineering equipment Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Data mining Type: general – SubjectFull: Corpora Type: general – SubjectFull: Knowledge graphs Type: general Titles: – TitleFull: An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lyu, Pin – PersonEntity: Name: NameFull: Yue, Yongyong – PersonEntity: Name: NameFull: Yu, Wengbing – PersonEntity: Name: NameFull: Xiao, Liqiao – PersonEntity: Name: NameFull: Liu, Chao – PersonEntity: Name: NameFull: Zheng, Pai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09544828 Numbering: – Type: volume Value: 36 – Type: issue Value: 11 Titles: – TitleFull: Journal of Engineering Design Type: main |
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