Named Entity Identification in the Power Dispatch Domain Based on RoBERTa-Attention-FL Model

Yan Chen (), Dezhao Lin, Qi Meng, Zengfu Liang and Zhixiang Tan
Additional contact information
Yan Chen: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Dezhao Lin: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Qi Meng: Guangxi Power Grid Co., Ltd., Nanning 530022, China
Zengfu Liang: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Zhixiang Tan: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Energies, 2023, vol. 16, issue 12, 1-13

Abstract: Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa’s middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa’s last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model’s performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%.

Keywords: power dispatching; named entity recognition; RoBERTa; self-attention mechanism; syntactic information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/12/4654/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/12/4654/ (text/html)

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:gam:jeners:v:16:y:2023:i:12:p:4654-:d:1169025

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().