Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Abstract

Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model’s ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers’ composition correction.

Semantic Coherence Analysis of English Texts Based on Sentence Semantic Graphs

“…In recent years, as deep learning has made significant gains in the field of artificial intelligence, researchers have experimented with deep learning to analyse the coherence of text, and some progress has been made. M Hung et al [21] used deep learning methods to construct a model for English text coherence analysis, which performed well in English text coherence diagnosis. Vasamsetti Srinivas et al [22] used deep neural networks to build pre-trained language models to learn text features, and also made research progress.…”

“…The data set consisted of the ICNALE [29], COLEN [30], CELC [31] and TECCL [32] corpora. One thousand articles were selected from the ICNALE corpus under the same essay topic as the test set for incoherent sentence extraction; 100 unlabelled plain-text English texts from the COLEN corpus were selected as the test set for sentence ranking experiments on this model.…”

Retracted: Application of Adaptive Neural Network Algorithm Model in English Text Analysis

“…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:…”