GR-WCM: Named Entity Recognition Algorithm in the Field of Wind Turbine Faults Based on Global Context and R-Drop

Youxing Zhang (), Xingfen Wang () and Meihua Wang ()
Additional contact information
Youxing Zhang: School of Computer Beijing Information Science and Technology University Beijing
Xingfen Wang: School of Information Management, Beijing Information Science and Technology University Beijing
Meihua Wang: School of Computer Beijing Information Science and Technology University Beijing

A chapter in LISS 2024, 2025, pp 228-240 from Springer

Abstract: Abstract This study aims to address the problem of named entity recognition (NER) in wind turbine fault texts. Currently, most wind turbine fault texts are characterized by an abundance of technical terms, significant long-distance dependencies, and unclear entity boundaries. Therefore, we propose an improved WC-LSTM model (GR-WCM). First, we use a self-attention mechanism to encode lexical information, capturing all lexical information for word-character fusion. Second, we introduce a Global Context Mechanism (GCM) that integrates the representation of the entire future and past sentences into each unit of the Bi-LSTM framework to obtain semantic features of long sequences. We also employ the R-drop training algorithm during model training to further enhance the model’s generalization ability. Finally, we use Conditional Random Fields (CRF) for sequence decoding to obtain the globally optimal sequence. Experimental results on a self-built wind turbine fault dataset and the Resume dataset show that, compared with the baseline WC-LSTM model, the proposed method improves the F1 score by 2.12% and 0.77%, respectively, effectively enhancing the accuracy of Chinese NER.

Keywords: name entity recognition; wind turbine faults; global context mechanism; R-Drop (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_19

Ordering information: This item can be ordered from
http://www.springer.com/9789819696970

DOI: 10.1007/978-981-96-9697-0_19

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().