Open AccessAn Application of Automatic Text Revision for Power Defect Log
Author(s) -
Hao Li,
Feng Deng,
Jixiang Lu,
Tao Zhang,
Hong Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012027
Subject(s) - workload , computer science , natural language processing , word (group theory) , vocabulary , process (computing) , field (mathematics) , artificial intelligence , meaning (existential) , information retrieval , data mining , database , linguistics , programming language , psychology , philosophy , mathematics , pure mathematics , psychotherapist , operating system
There are a large number of unstructured texts without data cleaning in the field of electric power. It is expensive to rely on manual ways to process the large amount of text data. In order to reduce the workload of data cleaning, we propose an intelligent method of automatic revision of power defect logs in this paper by adopting natural language processing technologies. We utilize entity recognition technology to recognize electrical equipment words on the text and utilize word similarity calculation to find out words with similar meaning in the standard vocabulary, which is the main process to revise Abnormal text. With the outstanding performance of entity recognition, the workload of data cleaning is reduced approximately 70% through our proposed method, which greatly improves the efficiency of unstructured data processing.