Cargando…
Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network
In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model i...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457992/ https://www.ncbi.nlm.nih.gov/pubmed/34567102 http://dx.doi.org/10.1155/2021/5100809 |
_version_ | 1784571227267923968 |
---|---|
author | Tang, Qiaohui |
author_facet | Tang, Qiaohui |
author_sort | Tang, Qiaohui |
collection | PubMed |
description | In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition. |
format | Online Article Text |
id | pubmed-8457992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84579922021-09-23 Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network Tang, Qiaohui Comput Intell Neurosci Research Article In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition. Hindawi 2021-09-15 /pmc/articles/PMC8457992/ /pubmed/34567102 http://dx.doi.org/10.1155/2021/5100809 Text en Copyright © 2021 Qiaohui Tang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Tang, Qiaohui Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title | Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title_full | Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title_fullStr | Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title_full_unstemmed | Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title_short | Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network |
title_sort | analysis of english multitext reading comprehension model based on deep belief neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457992/ https://www.ncbi.nlm.nih.gov/pubmed/34567102 http://dx.doi.org/10.1155/2021/5100809 |
work_keys_str_mv | AT tangqiaohui analysisofenglishmultitextreadingcomprehensionmodelbasedondeepbeliefneuralnetwork |