A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, t...
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Gespeichert in:

Autor*in:	Zhiqiang Zheng [verfasserIn] Minghao Liu [verfasserIn] Zhi Weng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	solid rocket engines named entity recognition BERT pre-trained language model dual-channel network model

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 3, p 752
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:3, p 752

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12030752

Katalog-ID:	DOAJ080664326

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allfieldsGer	10.3390/electronics12030752 doi (DE-627)DOAJ080664326 (DE-599)DOAJ9fdae561ee104deebc2e0677e4c63ad2 DE-627 ger DE-627 rakwb eng TK7800-8360 Zhiqiang Zheng verfasserin aut A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models. solid rocket engines named entity recognition BERT pre-trained language model dual-channel network model Electronics Minghao Liu verfasserin aut Zhi Weng verfasserin aut In Electronics MDPI AG, 2013 12(2023), 3, p 752 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:3, p 752 https://doi.org/10.3390/electronics12030752 kostenfrei https://doaj.org/article/9fdae561ee104deebc2e0677e4c63ad2 kostenfrei https://www.mdpi.com/2079-9292/12/3/752 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 3, p 752
allfieldsSound	10.3390/electronics12030752 doi (DE-627)DOAJ080664326 (DE-599)DOAJ9fdae561ee104deebc2e0677e4c63ad2 DE-627 ger DE-627 rakwb eng TK7800-8360 Zhiqiang Zheng verfasserin aut A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models. solid rocket engines named entity recognition BERT pre-trained language model dual-channel network model Electronics Minghao Liu verfasserin aut Zhi Weng verfasserin aut In Electronics MDPI AG, 2013 12(2023), 3, p 752 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:3, p 752 https://doi.org/10.3390/electronics12030752 kostenfrei https://doaj.org/article/9fdae561ee104deebc2e0677e4c63ad2 kostenfrei https://www.mdpi.com/2079-9292/12/3/752 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 3, p 752
language	English
source	In Electronics 12(2023), 3, p 752 volume:12 year:2023 number:3, p 752
sourceStr	In Electronics 12(2023), 3, p 752 volume:12 year:2023 number:3, p 752
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	solid rocket engines named entity recognition BERT pre-trained language model dual-channel network model Electronics
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container_title	Electronics
authorswithroles_txt_mv	Zhiqiang Zheng @@aut@@ Minghao Liu @@aut@@ Zhi Weng @@aut@@
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callnumber-first	T - Technology
author	Zhiqiang Zheng
spellingShingle	Zhiqiang Zheng misc TK7800-8360 misc solid rocket engines misc named entity recognition misc BERT pre-trained language model misc dual-channel network model misc Electronics A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines
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topic_title	TK7800-8360 A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines solid rocket engines named entity recognition BERT pre-trained language model dual-channel network model
topic	misc TK7800-8360 misc solid rocket engines misc named entity recognition misc BERT pre-trained language model misc dual-channel network model misc Electronics
topic_unstemmed	misc TK7800-8360 misc solid rocket engines misc named entity recognition misc BERT pre-trained language model misc dual-channel network model misc Electronics
topic_browse	misc TK7800-8360 misc solid rocket engines misc named entity recognition misc BERT pre-trained language model misc dual-channel network model misc Electronics
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title	A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines
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title_sort	chinese bert-based dual-channel named entity recognition method for solid rocket engines
callnumber	TK7800-8360
title_auth	A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines
abstract	With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models.
abstractGer	With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models.
abstract_unstemmed	With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models.
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title_short	A Chinese BERT-Based Dual-Channel Named Entity Recognition Method for Solid Rocket Engines
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