Abstract
Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment (namely, main transformer, circuit breaker, and isolating switch) are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method.
Highlights
With the development of artificial intelligence technology, extracting high-level structured semantic information such as entities, attributes, and relations from natural language to solve more advanced tasks in various industries is a prevailing research hot spot [1, 2]
It is coping with a large number of operation and maintenance records generated during the maintenance of various types of equipment
Deep learning-based approaches have enabled end-to-end named entity recognition via neural networks that no longer rely on manually defined features [15, 16]. e most commonly used method is based on character embedding, and the character embedding features are input into a long short-term memory (LSTM) with conditional random field (CRF) [8, 17]
Summary
With the development of artificial intelligence technology, extracting high-level structured semantic information such as entities, attributes, and relations from natural language to solve more advanced tasks in various industries is a prevailing research hot spot [1, 2]. E most commonly used method is based on character embedding, and the character embedding features are input into a long short-term memory (LSTM) with conditional random field (CRF) [8, 17] This method cannot represent the multiple meanings of a word. Based on the above analysis, we propose a Chinese named entity recognition method based on progressive multitype feature fusion (PMTFF) to recognize unstructured maintenance records of power primary equipment. Label 3452 unstructured maintenance records (EPE-MR dataset) (2) We employ the standard test reports, standard maintenance, and fault analysis reports of power primary equipment to train BERT model as the character embedding to obtain words representation ability (3) We propose a progressive multilevel radical feature extraction module (PML-RFE) to extract valuable semantic information
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