An Entity Recognition Approach of GIS Fault Text

Autor:	Chen Li, Junhua Hu, Shaohe Wang, Jiapeng Tian, Chen Xiaoxin
Rok vydání:	2021
Předmět:	Computer science Task analysis Bit error rate Data mining Representation (mathematics) computer.software_genre Fault (power engineering) Encoder computer Maintenance engineering Experience sharing Transformer (machine learning model)
Zdroj:	2021 Power System and Green Energy Conference (PSGEC).
DOI:	10.1109/psgec51302.2021.9542391
Popis:	Entity recognition of GIS fault text contributes to the manual experience sharing and the execution efficiency of GIS operation and maintenance. However, traditional models have poor performance on such entity recognition tasks. In this paper, a novel entity recognition model is proposed. The model consists of several single-type entity recognizers, which increase the accuracy of the model. Bidirectional Encoder Representation from Transformers (BERT) is also applied to understand the characters in the text. Experiments show that the proposed model can achieve F1-score of 88.0%. Compared with other traditional entity recognition models, the performance of the proposed model is improved by about 20%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8e572a040441aca24fa65ebcb3fa0f79 https://doi.org/10.1109/psgec51302.2021.9542391 Zobrazit plný text záznamu