Systems Analysis for Correcting English Compositions

Rizzo, Betty

doi:10.2307/357499

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“…Many students cannot get targeted guidance in the training, so there is an increasing demand for using the computer to correct English compositions. Some regular English writing self-correcting systems, such as Bingguo and Jouku, have been used in many colleges and universities, which greatly relieve the pressure of English teachers in teaching and correcting English compositions [3][4][5][6]. Moreover, in China's English database, the error rate of noun use is 8.4%, while the error rate of verb use is nearly twice, which is the most error-prone part of speech error.…”

Section: Introductionmentioning

confidence: 99%

English Grammar Error Detection Using Recurrent Neural Networks

Zhen-hui

2021

Scientific Programming

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Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers’ manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb’s grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection.

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Section: Introductionmentioning

confidence: 99%

English Grammar Error Detection Using Recurrent Neural Networks

Zhen-hui

2021

Scientific Programming

View full text Add to dashboard Cite

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Systems Analysis for Correcting English Compositions

Cited by 1 publication

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English Grammar Error Detection Using Recurrent Neural Networks

English Grammar Error Detection Using Recurrent Neural Networks

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