Lyu, Pin, Yue, Yongyong, Yu, Wengbing, Xiao, Liqiao, Liu, Chao and Zheng, Pai (2024). An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus. Journal of Engineering Design ,
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.
Publication DOI: | https://doi.org/10.1080/09544828.2024.2340392 |
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Divisions: | College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing |
Funding Information: | The authors wish to acknowledge the funding support from Shanghai Science and Technology Program under Grant 22010500900,the Mainland-Hong Kong Joint Funding Scheme of the Innovation and Technology Commission, Hong Kong Special Administration Region under |
Additional Information: | Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript version of the following article, accepted for publication in the Journal of Engineering Design and published on 1st July 2024. This version is made available under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Uncontrolled Keywords: | Named entity recognition,adaptive mechanism,bidirectional encoder representations from transformers,fuzzy boundary,multi-neural network collaboration,General Engineering |
Publication ISSN: | 1466-1837 |
Last Modified: | 30 Sep 2024 13:48 |
Date Deposited: | 02 Aug 2024 16:07 |
Full Text Link: | |
Related URLs: |
https://www.tan ... 28.2024.2340392
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2024-07-01 |
Published Online Date: | 2024-07-01 |
Accepted Date: | 2024-04-04 |
Authors: |
Lyu, Pin
Yue, Yongyong Yu, Wengbing Xiao, Liqiao Liu, Chao ( 0000-0001-7261-3832) Zheng, Pai |