{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T14:53:00Z","timestamp":1770994380716,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1004305"],"award-info":[{"award-number":["2018YFB1004305"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51865004"],"award-info":[{"award-number":["51865004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Project of Guizhou Province","doi-asserted-by":"publisher","award":["Talents [2018]5781"],"award-info":[{"award-number":["Talents [2018]5781"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Project of Science and Technology in Guizhou Province","award":["[2017]3004"],"award-info":[{"award-number":["[2017]3004"]}]},{"name":"Talent Project in Guizhou Province","award":["KY [2018]037"],"award-info":[{"award-number":["KY [2018]037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.<\/jats:p>","DOI":"10.3390\/sym12121986","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T20:10:22Z","timestamp":1606767022000},"page":"1986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Liguo","family":"Yao","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"},{"name":"Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haisong","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Business Administration, National Central University, Taoyuan 320003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Huan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoqiao","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Selangor 43400, Malaysia"},{"name":"Department of Mechanical and Electronic Engineering, Guizhou Communications Polytechnic, Guiyang 551400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s10845-016-1290-2","article-title":"Product design and manufacturing process based ontology for manufacturing knowledge reuse","volume":"30","author":"Chhim","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ymssp.2018.08.004","article-title":"Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing","volume":"117","author":"He","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, Q., Xie, Q., Yuan, Q., Huang, H., and Li, Y. (2019). Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model. Symmetry, 11.","DOI":"10.3390\/sym11101233"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"89278","DOI":"10.1109\/ACCESS.2019.2925561","article-title":"Real-time tiny part defect detection system in manufacturing using deep learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","article-title":"Segmentation-based deep-learning approach for surface-defect detection","volume":"31","author":"Tabernik","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compind.2019.04.018","article-title":"Industry 4.0: Emerging themes and future research avenues using a text mining approach","volume":"109","author":"Galati","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.patrec.2018.11.019","article-title":"Effective vector representation for the Korean named-entity recognition","volume":"117","author":"Kwon","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.jbi.2017.06.013","article-title":"Drugsemantics: A corpus for named entity recognition in spanish summaries of product characteristics","volume":"72","author":"Moreno","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37736","DOI":"10.1109\/ACCESS.2020.2973319","article-title":"Transfer Learning for Arabic Named Entity Recognition with Deep Neural Networks","volume":"8","author":"Jararweh","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.engappai.2019.05.007","article-title":"SANE 2.0: System for fine grained named entity typing on textual data","volume":"84","author":"Lal","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, X., Lv, J., Xie, Q., Huang, H., and Wang, W. (2020). Construction and application of an ergonomic simulation optimization method driven by a posture load regulatory network. SIMULATION TTrans. Soc. Modeling Simul. Int., 96.","DOI":"10.1177\/0037549720915261"},{"key":"ref_12","unstructured":"Wu, Z., Liu, W., Zheng, W., Nie, W., and Li, Z. (2020). Manufacturing process similarity measurement model and application based on process constituent elements. Int. J. Prod. Res., 1\u201323."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4867","DOI":"10.1007\/s11042-018-6808-5","article-title":"A comprehensive overview of feature representation for biometric recognition","volume":"79","author":"Rida","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_16","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2020, July 20). Distributed representations of words and phrases and their compositionality. In Proceedings of the Advances in Neural Information Processing Systems, Available online: https:\/\/proceedings.neurips.cc\/paper\/2013\/hash\/9aa42b31882ec039965f3c4923ce901b-Abstract.html."},{"key":"ref_17","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2020, July 10). Sequence to sequence learning with neural networks. In Proceedings of the Advances in neural Information Processing Systems, Available online: https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yao, L., Huang, H., and Chen, S.-H. (2020). Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules. Processes, 8.","DOI":"10.3390\/pr8070751"},{"key":"ref_19","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018, November 19). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"ref_20","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Li, Y.-K., Feng, S., Tian, H., Wu, H., and Wang, H. (2020, January 7\u201312). ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding. Proceedings of the AAAI, New York, NY, USA.","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"ref_22","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., and Le, Q.V. (2020, June 15). Xlnet: Generalized autoregressive pretraining for language understanding. In Proceedings of the Advances in Neural Information Processing Systems, Available online: https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html."},{"key":"ref_23","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110572","DOI":"10.1016\/j.jss.2020.110572","article-title":"Improving software bug-specific named entity recognition with deep neural network","volume":"165","author":"Zhou","year":"2020","journal-title":"J. Syst. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s12145-019-00390-3","article-title":"BiLSTM-CRF for geological named entity recognition from the geoscience literature","volume":"12","author":"Qiu","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Georgescu, T.-M., Iancu, B., and Zurini, M. (2019). Named-Entity-Recognition-Based Automated System for Diagnosing Cybersecurity Situations in IoT Networks. Sensors, 19.","DOI":"10.3390\/s19153380"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vardhan, H., Surana, N., and Tripathy, B. (2020, January 13\u201315). Named-Entity Recognition for Legal Documents. Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, Jaipur, India.","DOI":"10.1007\/978-981-15-3383-9_43"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103289","DOI":"10.1016\/j.jbi.2019.103289","article-title":"Chinese clinical named entity recognition with radical-level feature and self-attention mechanism","volume":"98","author":"Yin","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, H., and Sun, X. (2017, January 4\u20139). A unified model for cross-domain and semi-supervised named entity recognition in chinese social media. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"ref_30","first-page":"648","article-title":"Multi-neural network collaboration for Chinese military named entity recognition","volume":"60","author":"Yin","year":"2020","journal-title":"J. Tsinghua Univ. (Sci. Technol.)"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jin, B., Cruz, L., and Gon\u00e7alves, N. (2020). Deep Facial Diagnosis: Deep Transfer Learning from Face Recognition to Facial Diagnosis. IEEE Access.","DOI":"10.1109\/ACCESS.2020.3005687"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shen, Y., Yun, H., Lipton, Z.C., Kronrod, Y., and Anandkumar, A. (2017). Deep active learning for named entity recognition. arXiv.","DOI":"10.18653\/v1\/W17-2630"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.neunet.2019.08.032","article-title":"Named entity recognition in electronic health records using transfer learning bootstrapped neural networks","volume":"121","author":"Gligic","year":"2020","journal-title":"Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.knosys.2017.06.023","article-title":"A combination of active learning and self-learning for named entity recognition on Twitter using conditional random fields","volume":"132","author":"Tran","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kung, H.-K., Hsieh, C.-M., Ho, C.-Y., Tsai, Y.-C., Chan, H.-Y., and Tsai, M.-H. (2020). Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning. Appl. Sci., 10.","DOI":"10.3390\/app10124234"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, M., Geng, G., and Chen, J. (2020). Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations. Entropy, 22.","DOI":"10.3390\/e22020252"},{"key":"ref_37","unstructured":"Liu, M., Tu, Z., Wang, Z., and Xu, X. (2020). Ltp: A new active learning strategy for bert-crf based named entity recognition. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/72.963769","article-title":"LSTM recurrent networks learn simple context-free and context-sensitive languages","volume":"12","author":"Gers","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.patrec.2018.04.033","article-title":"Palmprint recognition with an efficient data driven ensemble classifier","volume":"126","author":"Rida","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","unstructured":"Xu, L., Dong, Q., Yu, C., Tian, Y., Liu, W., Li, L., and Zhang, X. (2020). CLUENER2020: Fine-grained Name Entity Recognition for Chinese. arXiv."},{"key":"ref_41","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.compbiomed.2019.04.002","article-title":"Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition","volume":"108","author":"Xu","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, J., Xia, C., Yan, H., and Xu, W. (2020). Innovative Deep Neural Network Modeling for Fine-grained Chinese Entity Recognition. Electronics, 9.","DOI":"10.3390\/electronics9061001"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bo, W., Wei, W., Yang, W., Xuefeng, W., and Caiwei, L. (2020, January 28\u201330). Event Recognition in Chinese Emergencies Corpus Using ALBERT-BiLSTM-CRF. Proceedings of the 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS50287.2020.9202269"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"113504","DOI":"10.1016\/j.eswa.2020.113504","article-title":"NI-MWMOTE: An Improving Noise-immunity Majority Weighted Minority Oversampling Technique for Imbalanced Classification Problems","volume":"158","author":"Wei","year":"2020","journal-title":"Expert Syst. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1986\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:39:45Z","timestamp":1760179185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/12\/1986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["sym12121986"],"URL":"https:\/\/doi.org\/10.3390\/sym12121986","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}