Research on Spoken Language Understanding Based on Deep Learning

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified...

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Autor principal:	Hui Yanli
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/19cf4bea454a45b6b99e50a076fb6354
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id	oai:doaj.org-article:19cf4bea454a45b6b99e50a076fb6354
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spelling	oai:doaj.org-article:19cf4bea454a45b6b99e50a076fb63542021-11-08T02:36:19ZResearch on Spoken Language Understanding Based on Deep Learning1875-919X10.1155/2021/8900304https://doaj.org/article/19cf4bea454a45b6b99e50a076fb63542021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8900304https://doaj.org/toc/1875-919XAiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.Hui YanliHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Hui Yanli Research on Spoken Language Understanding Based on Deep Learning
description	Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.
format	article
author	Hui Yanli
author_facet	Hui Yanli
author_sort	Hui Yanli
title	Research on Spoken Language Understanding Based on Deep Learning
title_short	Research on Spoken Language Understanding Based on Deep Learning
title_full	Research on Spoken Language Understanding Based on Deep Learning
title_fullStr	Research on Spoken Language Understanding Based on Deep Learning
title_full_unstemmed	Research on Spoken Language Understanding Based on Deep Learning
title_sort	research on spoken language understanding based on deep learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/19cf4bea454a45b6b99e50a076fb6354
work_keys_str_mv	AT huiyanli researchonspokenlanguageunderstandingbasedondeeplearning
_version_	1718443069801496576

Research on Spoken Language Understanding Based on Deep Learning

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