21
Views
0
CrossRef citations to date
0
Altmetric
Research Article

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

, , , , &
Received 29 Aug 2023, Accepted 04 Apr 2024, Published online: 01 Jul 2024

References

  • Aman, K., and S. Binil. 2021. “FabNER: Information Extraction from Manufacturing Process Science Domain Literature Using Named Entity Recognition.” Journal of Intelligent Manufacturing 33:2393–2407.
  • Bhattacharya, K., and A. Chakrabarti. 2023. “A Knowledge Graph and Rule Based Reasoning Method for Extracting Sapphire Information from Text.” In Proceedings of the International Conference on Engineering Design, 24–28.
  • Cheng, Chen, and Beshoy Morkos. 2023. “Exploring Topic Modelling for Generalising Design Requirements in Complex Design.” Journal of Engineering Design 34 (11): 922–940. https://doi.org/10.1080/09544828.2023.2268850
  • Devlin, J., M. Chang, K. Lee, and K. Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceeding Conference North American Chapter Association Computational Linguistics: Human Language Technolgies, 4171–4186.
  • Ghaddar, A., and P. Langlais. 2018. “Robust Lexical Features for Improved Neural Network Named-Entity Recognition.” In Proceeding 27th International Conference Computional Linguistics, 1896–1907.
  • Gridach, M. 2017. “Character-Level Neural Network for Biomedical Named Entity Recognition.” Journal of Biomedical Informatics 70:85–91. https://doi.org/10.1016/j.jbi.2017.05.002
  • Habibi, M., L. Weber, M. Neves, D. L. Wiegandt, and U. Leser. 2017. “Deep Learning with Word Embeddings Improves Biomedical Named Entity Recognition.” Bioinformatics 33 (14): i37–i48.
  • He, L., X. Zhang, Z. Li, P. Xiao, Z. Wei, X. Cheng, and S. Qu. 2022. “A Chinese Named Entity Recognition Model of Maintenance Records for Power Primary Equipment Based on Progressive Multitype Feature Fusion.” Complexity 1: 8114217.
  • Huang, Z., W. Xu, and K. Yu. 2015. “Bidirectional LSTM-CRF models for sequence tagging.” arXiv:1508.01991.
  • Jiang, Pingfei, Mark Atherton, and Salvatore Sorce. 2023. “Extraction and Linking of Motivation, Specification and Structure of Inventions for Early Design use.” Journal of Engineering Design 34 (5-6): 411–436. https://doi.org/10.1080/09544828.2023.2227934
  • Jiang, Shuo, and Jianxi Luo. 2022. “Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data.” Journal of Engineering Design 33 (10): 716–727. https://doi.org/10.1080/09544828.2022.2143155
  • Jie, Z., and W. Lu. 2018. “Dependency-Guided LSTM-CRF for Named Entity Recognition.” In Proceeding Conference Empirical Methods Natural Language Processing, 3860–3870.
  • Ju, M., M. Miwa, and S. Ananiadou. 2018. “A Neural Layered Model for Nested Named Entity Recognition.” In Proceeding Conference North American Chapter Association Computational Linguistics: Human Language Technologies, 1:1446–1459.
  • Katiyar, A., and C. Cardie. 2018. “Nested Named Entity Recognition Revisited.” In Proceeding Annual Meeting Association Computational Linguistics 1 (1): 861–871.
  • Kuru, O., O. A. Can, and D. Yure. 2016. “CharNER: Character-Level Named Entity Recognition.” In Proceeding 26th International Conference Computational Linguistics, 911–921.
  • Lample, G., M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer. 2016. “Neural Architectures for Named Entity Recognition.” In Proceeding Conference North American Chapter Association Computational Linguistics, 260–270.
  • Lecun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521: 436–444.
  • Li, P. H., R. P. Dong, Y. S. Wang, J. C. Chou, and W. Y. Ma. 2017. “Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks.” In Proceeding Conference Empirical Methods Natural Language Processing, 2664–2669.
  • Li, J., A. Sun, J. Han, and C. Li. 2022. “A Survey on Deep Learning for Named Entity Recognition.” IEEE Transactions on Knowledge and Data Engineering 34:50–70. https://doi.org/10.1109/TKDE.2020.2981314
  • Liang, K., B. Zhou, Y. Zhang, Y. He, X. Guo, and B. Zhang. 2022. “A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT.” Electronics 11:979. https://doi.org/10.3390/electronics11070979
  • Lin, Y., L. Liu, H. Ji, D. Yu, and J. Han. 2019. “Reliability-Aware Dynamic Feature Composition for Name Tagging.” In Proceeding 57th Annual Meeting Association Computational Linguistics, 165–174.
  • Liu, K., L. Fang, L. Liu, and Y. Han. 2007. “Implementation of a Kernel-Based Chinese Relation Extraction System.” Journal of Computer Research and Development 8:136–141.
  • Lyu, P., K. Zhang, W. Yu, B. Wang, and C. Liu. 2022b. “A Novel RSG-Based Intelligent Bearing Fault Diagnosis Method for Motors in High-Noise Industrial Environment.” Advanced Engineering Informatics 52:101564. https://doi.org/10.1016/j.aei.2022.101564
  • Lyu, P., P. Zheng, C. Liu, and M. Xia. 2022a. “A Novel Multiview Sampling-Based Meta Self-Paced Learning Approach for Class-Imbalanced Intelligent Fault Diagnosis.” IEEE Transactions on Instrumentation and Measurement 71:1–12.
  • Ma, X., and E. Hovy. 2016. “End-to-End Sequence Labeling via Bidirectional LSTM-CNNs-CRF.” In Proceeding 54th Annual Meeting Association Computational Linguistics, 1064–1074.
  • Nadeau, D., and S. Sekine. 2007. “A Survey of Named Entity Recognition and Classification.” Lingvisticae Investigationes 30 (1): 3–26. https://doi.org/10.1075/li.30.1.03nad
  • Radford, A., K. Narasimhan, T. Salimans, and I. Sutskever. 2018. “Improving Language Understanding by Generative pre-Training.” Open AI Technical Report, 1–12.
  • Rei, M., G. K. Crichton, and S. Pyysalo. 2016. “Attending to Characters in Neural Sequence Labeling Models.” In Proceeding 26th International Conference Computational Linguistics, 309–318.
  • Rosa, R. L., G. M. Schwartz, W. V. Ruggiero, and D. Z. Rodríguez. 2019. “A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning.” IEEE Transactions on Industrial Informatics 15:2124–2135. https://doi.org/10.1109/TII.2018.2867174
  • Sarica, Serhad, Jianxi Luo, and Kristin L. Wood. 2020. “TechNet: Technology Semantic Network Based on Patent Data.” Expert Systems with Applications 142:112995. https://doi.org/10.1016/j.eswa.2019.112995
  • Shen, Y., H. Yun, Z. C. Lipton, Y. Kronrod, and A. Anandkumar. 2017. “Deep Active Learning for Named Entity Recognition.” In Proceeding 2nd Workshop Representation Learning, 252–256.
  • Siddharth, L., L. T. Blessing, K. L. Wood, and J. Luo. 2022. “Engineering Knowledge Graph from Patent Database.” Journal of Computing and Information Science in Engineering 22 (2): 021008. https://doi.org/10.1115/1.4052293
  • Thorne, C., and S. Akhondi. 2020. “Word Embeddings for Chemical Patent Natural Language Processing.” arXiv:2010.12912v1.
  • Tomori, S., T. Ninomiya, and S. Mori. 2016. “Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks.” In Proceeding 54th Annual Meeting Association Computational Linguistics, 2:236–242.
  • Tran, Q., A. MacKinlay, and A. J. Yepes. 2017. “Named Entity Recognition with Stack Residual LSTM and Trainable Bias Decoding.” In Proceeding 8th International Joint Conference Natural Language Processing, 566–575.
  • Xia, L., Y. Liang, J. Leng, and P. Zheng. 2023. “Maintenance Planning Recommendation of Complex Industrial Equipment Based on Knowledge Graph and Graph Neural Network.” Reliability Engineering & System Safety 232:109068. https://doi.org/10.1016/j.ress.2022.109068
  • Xia, L., P. Zheng, X. Li, R. X. Gao, and L. Wang. 2022. “Toward Cognitive Predictive Maintenance: A Survey of Graph-Based Approaches.” Journal of Manufacturing Systems 64:107–120. https://doi.org/10.1016/j.jmsy.2022.06.002
  • Yan, P. 2011. “Research on the Identification for Chinese Named Entity Based on Combination of Rules and Statistic Analysis.” Computer Digital Engineering 39:88–92.
  • Zhai, F., P. Saloni, B. Xiang, and B. Zhou. 2017. “Neural Models for Sequence Chunking.” Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.
  • Zhang, J., M. Z. A. Bhuiyan, X. Yang, and K. S. Amit. 2022. “Trustworthy Target Tracking with Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT.” IEEE Transactions on Industrial Informatics 18:1301–1309. https://doi.org/10.1109/TII.2021.3098317
  • Zheng, P., L. Xia, C. Li, X. Li, and B. Liu. 2021. “Towards Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph-Based Multi-Agent Reinforcement Learning Approach.” Journal of Manufacturing Systems 61:16–26. https://doi.org/10.1016/j.jmsy.2021.08.002
  • Zuo, Haoyu, Q. Jing, T. Song, L. Sun, P. Childs, and Liuqing Chen. 2022. “WikiLink: An Encyclopedia-Based Semantic Network for Design Creativity.” Journal of Intelligence 10 (4): 103. https://doi.org/10.3390/jintelligence10040103.
  • Zuo, H., Y. Yin, and P. Childs. 2022. “Patent-KG: Patent Knowledge Graph Extraction for Engineering Design.” Proceedings of the Design Society 2:821–830. https://doi.org/10.1017/pds.2022.84

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.