NAMED ENTITY RECOGNITION IN REFRACTORY HIGH-ENTROPY ALLOYS USING DEEP LEARNING

Samara National Research University named after Academician S. P. Korolev

doi:10.24412/2413-7383-2025-3-38-4-10

Samara National Research University named after Academician S. P. Korolev

About the authors

Samara National Research University named after Academician S. P. Korolev

Hao Hu – PhD student, Samara National Research University. Research fields: Machine Learning, Metallic Materials, Metallurgical Engineering.: +86 18104860834 (China) / +7 980 908 3470 (Russia). ORCID: 0009-0004-2902-2593

UDC: 519.4
DOI: 10.24412/2413-7383-2025-3-38-4-10
Language: Russian

Abstract: В ответ на быстрый рост объема литературы в области тугоплавких высокоэнтропийных сплавов (RHEAs) и низкую эффективность извлечения ключевой информации в данной статье предлагается полуавтоматический рабочий процесс извлечения информации. Метод использует большую

Keywords: Refractory high-entropy alloys, named entity recognition, large language model, BERT-BiLSTM-CRF.

References

Miracle, D. B. Senkov, O. N. A critical review of high entropy alloys and related concepts. Acta Materialia. 2017, 122: 448–511.
Hearst, M. A. Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th Conference on Computational Linguistics (COLING). 1992: 539–545.
Lafferty, J. McCallum, A. Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning (ICML). 2001: 282–289.
Huang, Z. Xu, W. Yu, K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint. arXiv:1508.01991, 2015.
Devlin, J. Chang, M. W. Lee, K. Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT. 2019: 4171–4186.
Kim, E. Huang, K. Jegelka, S. Olivetti, E. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Computational Materials. 2017, 3: 53.
Trewartha, A. Dagdelen, J. Huo, H. Cruse, K. Riebesell, J. Jain, A. Ceder, G. Persson, K. A. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns. 2022, 3(4): 100488.
Gupta, T. Trewartha, A. Cruse, K. Dagdelen, J. Huo, H. Ceder, G. Jain, A. Persson, K. A. MatSciBERT: A materials domain language model for text mining and information extraction. npj Computational Materials. 2022, 8: 102.

Issue: 3(38)'2025
Section: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
How to cite: Samara National Research University named after Academician S. P. Korolev. NAMED ENTITY RECOGNITION IN REFRACTORY HIGH-ENTROPY ALLOYS USING DEEP LEARNING // Problems of Artificial Intelligence. - 2025. - № 3 (38). - P. 4-10. - https://paijournal.guiaidn.ru/en/2025/3(38)-1.html


	РУС ENG
	NAMED ENTITY RECOGNITION IN REFRACTORY HIGH-ENTROPY ALLOYS USING DEEP LEARNING
About the magazine News Goals and sphere Founder and publisher Editorial Board Licensing conditions Confidentiality Attitude towards plagiarism Publication ethics Archiving Policy Subscription For authors Instructions for authors The review process Copyright Agreement on the transfer of rights Editorial fees Archive All issues Search Contacts Contacts	Samara National Research University named after Academician S. P. Korolev About the authors Samara National Research University named after Academician S. P. Korolev Hao Hu – PhD student, Samara National Research University. Research fields: Machine Learning, Metallic Materials, Metallurgical Engineering.: +86 18104860834 (China) / +7 980 908 3470 (Russia). ORCID: 0009-0004-2902-2593 UDC: 519.4 DOI: 10.24412/2413-7383-2025-3-38-4-10 Language: Russian Abstract: В ответ на быстрый рост объема литературы в области тугоплавких высокоэнтропийных сплавов (RHEAs) и низкую эффективность извлечения ключевой информации в данной статье предлагается полуавтоматический рабочий процесс извлечения информации. Метод использует большую Keywords: Refractory high-entropy alloys, named entity recognition, large language model, BERT-BiLSTM-CRF. References Miracle, D. B. Senkov, O. N. A critical review of high entropy alloys and related concepts. Acta Materialia. 2017, 122: 448–511. Hearst, M. A. Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th Conference on Computational Linguistics (COLING). 1992: 539–545. Lafferty, J. McCallum, A. Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning (ICML). 2001: 282–289. Huang, Z. Xu, W. Yu, K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint. arXiv:1508.01991, 2015. Devlin, J. Chang, M. W. Lee, K. Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT. 2019: 4171–4186. Kim, E. Huang, K. Jegelka, S. Olivetti, E. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Computational Materials. 2017, 3: 53. Trewartha, A. Dagdelen, J. Huo, H. Cruse, K. Riebesell, J. Jain, A. Ceder, G. Persson, K. A. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns. 2022, 3(4): 100488. Gupta, T. Trewartha, A. Cruse, K. Dagdelen, J. Huo, H. Ceder, G. Jain, A. Persson, K. A. MatSciBERT: A materials domain language model for text mining and information extraction. npj Computational Materials. 2022, 8: 102. Issue: 3(38)'2025 Section: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING How to cite: Samara National Research University named after Academician S. P. Korolev. NAMED ENTITY RECOGNITION IN REFRACTORY HIGH-ENTROPY ALLOYS USING DEEP LEARNING // Problems of Artificial Intelligence. - 2025. - № 3 (38). - P. 4-10. - https://paijournal.guiaidn.ru/en/2025/3(38)-1.html
Founder and publisher: State Institution «Institute of Problems of Artificial Intelligence» Address: 83048, Donetsk, Artema st.,118 Editor-in-chief: V. Ju. Shelepov. © State Institution «Institute of Problems of Artificial Intelligence»,