[Retracted] Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Abstract

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.

Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

“…Block recognition is the main element of shallow analysis, which can be applied to information retrieval, machine translation, subject content analysis, and text processing, and the accuracy of block recognition is directly related to the correctness of text analysis and text processing. Since [1] proposed a strategy for shallow syntactic analysis and designed and implemented a discourse block recognizer, shallow syntactic analysis has received general attention, and the theme of the 2018 CONLL conference was shallow syntactic analysis [2]. Public training and test set were given at this conference.…”

Retracted: Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”