Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

Named Entity Recognition (NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. The prefix Att means the model is based on atten...

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Autores principales:	Kaihong Zheng, Lingyun Sun, Xin Wang, Shangli Zhou, Hanbin Li, Sheng Li, Lukun Zeng, Qihang Gong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Power metering attention mechanism joint feature named entity recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9b0ead4904514bc99ac9df450241d6f9
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id	oai:doaj.org-article:9b0ead4904514bc99ac9df450241d6f9
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spelling	oai:doaj.org-article:9b0ead4904514bc99ac9df450241d6f92021-11-20T00:00:49ZNamed Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism2169-353610.1109/ACCESS.2021.3123154https://doaj.org/article/9b0ead4904514bc99ac9df450241d6f92021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585441/https://doaj.org/toc/2169-3536Named Entity Recognition (NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. The prefix Att means the model is based on attention mechanism. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the relationship of local context. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. The novelties of our work are the five layer model structure and the attention mechanism. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.Kaihong ZhengLingyun SunXin WangShangli ZhouHanbin LiSheng LiLukun ZengQihang GongIEEEarticlePower meteringattention mechanismjoint featurenamed entity recognitionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152564-152573 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Power metering attention mechanism joint feature named entity recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Power metering attention mechanism joint feature named entity recognition Electrical engineering. Electronics. Nuclear engineering TK1-9971 Kaihong Zheng Lingyun Sun Xin Wang Shangli Zhou Hanbin Li Sheng Li Lukun Zeng Qihang Gong Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
description	Named Entity Recognition (NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. The prefix Att means the model is based on attention mechanism. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the relationship of local context. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. The novelties of our work are the five layer model structure and the attention mechanism. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.
format	article
author	Kaihong Zheng Lingyun Sun Xin Wang Shangli Zhou Hanbin Li Sheng Li Lukun Zeng Qihang Gong
author_facet	Kaihong Zheng Lingyun Sun Xin Wang Shangli Zhou Hanbin Li Sheng Li Lukun Zeng Qihang Gong
author_sort	Kaihong Zheng
title	Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
title_short	Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
title_full	Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
title_fullStr	Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
title_full_unstemmed	Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism
title_sort	named entity recognition in electric power metering domain based on attention mechanism
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/9b0ead4904514bc99ac9df450241d6f9
work_keys_str_mv	AT kaihongzheng namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT lingyunsun namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT xinwang namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT shanglizhou namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT hanbinli namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT shengli namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT lukunzeng namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism AT qihanggong namedentityrecognitioninelectricpowermeteringdomainbasedonattentionmechanism
_version_	1718419851907694592

Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

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