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| Named Entity Recognition Method for Metallurgical Literature Based on Domain Knowledge Fusion and Phrase Structure Constraints |
| CHEN Wei1,2, YU Zhengtao1,2, WANG Zhenhan1,2 |
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504
2. Key Laboratory of Artificial Intelligence in Yunnan Province, Kunming University of Science and Technology, Kunming 650500 |
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Abstract Metallurgical named entity recognition(NER) aims to identify relevant entities such as metallurgical techniques, processes, terminologies, metallic elements and institutions in the texts of metallurgical domain. Metallurgical NER serves as the foundation for knowledge extraction and organization, hotspot detection, and information retrieval in this field. However, the scarcity of annotated data, the significant differences in entity types compared to general domains and long entities make the transfer of general domain NER models to the metallurgical field challenging. A named entity recognition method for metallurgical literature based on domain knowledge integration and phrase structure constraints is proposed. By fine-tuning the model with a small amount of annotated metallurgical data, the understanding of entity structures and related knowledge in the metallurgical domain is enhanced. During fine-tuning, a metallurgical domain dictionary is leveraged at the representation layer. Through character-word matching, domain-specific knowledge is incorporated into the representation layer to improve the transferability of the model. A phrase structure constraint module is designed to address the challenge of recognizing long entities. Character-level input sequences are matched with metallurgical-specific entity rules, and thus the entities conforming to the unique structures of metallurgical named entities are recognized. Experiments on metallurgical datasets indicate an accuracy improvement for the proposed method.
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Received: 11 October 2024
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| Fund:National Natural Science Foundation of China(No.U21B2027), Yunnan Fundamental Research Projects(No.202401AT070361), Open Fund of Yunnan Provincial Key Laboratory of Computer Technology Application(No.140520200151) |
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Corresponding Authors:
YU Zhengtao, Ph.D., professor. His research interests include natural language processing and machine translation.
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About author:: CHEN Wei, Ph.D., lecturer. His research interests include natural language processing, text mining and information retrie-val.
WANG Zhenhan, Ph.D., lecturer. His research interests include natural language processing and machine translation. |
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2024, Vol. 37 
