[Retracted] English Grammar Detection Based on LSTM‐CRF Machine Learning Model

Abstract

Deep learning and neural network have been widely used in the field of speech, vocabulary, text, pictures, and other information processing fields, which has achieved excellent research results. Neural network algorithm and prediction model were used in this paper for the study and exploration of English grammar. Aiming at the application requirements of English grammar accuracy and standardization, we proposed a machine learning model based on LSTM-CRF to detect and analyze English grammar. This paper briefly summarized the development trend of deep learning and neural network algorithm and designed the structure pattern of radial basis function neural network in grammar semantic detection and analysis on the basis of deep learning artificial neural network theory. Based on the morphological features of English grammar, a grammar database was established according to the rules of English word segmentation. In this paper, we proposed an improved conditional random field CRF (Conditional Random Field) network model based on LSTM (Long Short-Term Memory) neural network. It can improve the problem that the traditional machine learning model relies on feature point selection in English grammar detection. The machine learning model based on LSTM-CRF was used to recognize English grammar text entities. The results show that the English grammar detection system based on the LSTM-CRF model can simplify the process structure in the recognition process, reduce the unnecessary operation cycle, and improve the overall detection accuracy.

Environmental Affection‐Driven English Tense Analysis: A Healthcare Exercise‐Based Corpus Case Study over Public English Environment

“…The LSTM model is used to deal with the problem of sequence labeling, which can make full use of the information of the entire text sequence, including the relationship information between each word, and use this information for the processing of each word [ 23 , 24 ]. An LSTM model contains many LSTM cells, each LSTM cell contains an input gate, an output gate, a forget gate, and a memory cell [ 25 , 26 ].…”

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“…There are many techniques that can be used in this phase since every dataset and every model has its own functionality, hence the different dataset may require different techniques. For instance, work by [8] used 3 different techniques which are Word Segmentation, Character Digitization and Vector Construction while work by [10] only used Normalization technique.…”

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“…Performance evaluation can be seen through parameters used for detection values. Research by [8] uses Accuracy, Return Efficiency and F1 Index where its algorithm showed improved accuracy and efficiency rate. Work by [9] measures using Accuracy, Specificity, Sensitivity, Error and False Positive Rate which proposed algorithm has high returns.…”

Retracted: English Grammar Detection Based on LSTM‐CRF Machine Learning Model

“…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 …”