{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:07:25Z","timestamp":1753880845209,"version":"3.41.2"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"name":"the Science and Technology Projects of Guangxi Power Grid Co., Ltd","award":["040100KK52222059"],"award-info":[{"award-number":["040100KK52222059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,3,15]]},"abstract":"<jats:p> Named entity recognition is a crucial task in natural language processing, serving downstream tasks such as information extraction and question-answering systems. With the success of Transformer in the field of natural language processing, named entity recognition has made significant strides. However, despite these advancements, the recognition capability in specialized domains remains challenging. The distribution network system contains a vast amount of equipment maintenance text, characterized by inherent complexity and specialization, lacking effective recognition and extraction methods. To address this issue, we propose a vocabulary-enhanced named entity recognition method. Unlike the original named entity recognition methods, we employ a vocabulary adapter to enhance the understanding of text semantic information by integrating matching information between the dictionary and the text. This method strengthens the ability to extract specialized nouns. In conjunction with the TaCL-BERT pre-trained model, we conducted training on our self-constructed distribution network maintenance dataset, GNP-M, to better align the model with the context and specific entities of the electrical grid maintenance domain. Simultaneously, we compared our method with four other named entity recognition models, revealing satisfactory performance results. <\/jats:p>","DOI":"10.1142\/s0218126625501002","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T12:20:39Z","timestamp":1727266839000},"source":"Crossref","is-referenced-by-count":0,"title":["Vocabulary-Enhanced Named Entity Recognition and its Application on Distribution Network Maintenance"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7275-7090","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Nanning Power Supply Bureau of Guangxi Power Grid, Nanning 530031, Guangxi, P. R. 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