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- Title
Named Entity Recognition for Mine Electromechanical Equipment Monitoring Text.
- Authors
QIU Yunfei; XING Haoran; YU Zhilong; ZHANG Wenwen
- Abstract
The correct extraction of equipment name, parameter standard, fault location, fault type and other entities in the monitoring text of mine electromechanical equipment can assist experts to find abnormal equipment as soon as possible and improve the efficiency and accuracy of equipment fault analysis. In view of the fact that most entities in the field of mine electromechanical equipment are nested entities with long characters and strong contextual relevance, an entity recognition method combining multi-granularity features is proposed in this paper. The long sequence nested entity boundary is initially determined by the machine reading comprehension framework, and the context association representation between entities is deeply explored by BiLSTM neural network integrating attention mechanism. The experimental results show that this method has a good recognition effect on the entities in the mine electromechanical equipment monitoring text, and improves the effectiveness of other named entity recognition tasks in low resource scenarios.
- Subjects
FAULT location (Engineering); READING comprehension; TEXT mining; TEXT recognition
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 11, p129
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2302-0246