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Studies from China Southern Power Grid in the Area of Machine Learning Published (Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition)

  • Oct 15, 2021
  • 208 views
Source: 
Daily Asia Business

2021 OCT 14 (NewsRx) -- By a News Reporter-Staff News Editor at Daily Asia Business -- A new study on machine learning is now available. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making.”

Financial supporters for this research include China Southern Power Grid Company Ltd.; Digital Grid Research Institute, China Southern Power Grid.

The news editors obtained a quote from the research from China Southern Power Grid: “To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities’ names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Bi-order-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF.”

According to the news editors, the research concluded: “Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.”

For more information on this research see: Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition. IEEE Access, 2021,9():133491-133499. (IEEE Access - http://ieeexplore.ieee.org/servlet/opac?punumber=6287639). The publisher for IEEE Access is IEEE.

A free version of this journal article is available at https://doi.org/10.1109/ACCESS.2021.3112541.

Our news journalists report that additional information may be obtained by contacting Kaihong Zheng, China Southern Power Grid, Digital Grid Research Institute, Guangzhou, People’s Republic of China. Additional authors for this research include Jingfeng Yang, Lukun Zeng, Qihang Gong, Sheng Li, Shangli Zhou.

 

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