An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus

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.

KEYWORDS:

• Named entity recognition
• multi-neural network collaboration
• fuzzy boundary
• adaptive mechanism
• bidirectional encoder representations from transformers

Acknowledgements

Pin Lyu: Writing-original draft, Methodology, Funding acquisition. Yongyong Yue: Experiments, Validation. Pai Zheng: Conceptualisation, Methodology, Supervision, Funding acquisition. Wengbing Yu: Data curation. Liqiao Xia: Writing-review & editing. Chao Liu: Formal analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

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 Grant MHX/001/20, National Key R&D Programs of Cooperation on Science and Technology Innovation with Hong Kong, Macao and Taiwan under Grant SQ2020YFE020182 by the Ministry of Science and Technology of China, National Natural Science Foundation of China under Grant 52105534.