School of Foreign Languages, Xinyu University, Xinyu, Jiangxi 338004, China.
Comput Intell Neurosci. 2021 Oct 16;2021:6052873. doi: 10.1155/2021/6052873. eCollection 2021.
With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.
随着神经网络技术的飞速发展，我们已经在各个领域广泛应用了这项技术。在语言翻译领域，英语动词语法错误自动检测技术的研究正处于一个热门阶段。传统的人工检测已经不能适应当前的环境。因此，本文提出了一种基于递归神经网络（RNN）算法的英语动词语法错误自动检测技术，以解决这个问题。首先，比较了传统人工检测和递归神经网络 RNN 算法的准确性和反馈速度。其次，设计了一个可以根据语法顺序结合上下文进行计算的检测模型。最后，当输出动词结果与原文不一致时，自动标记错误检测效果。实验结果表明，本文研究的算法模型可以有效地提高检测精度和反馈效率，比传统的人工检测方法更适用、更有效。
