Part-of-speech tagging for English composition is the basis for automatic correction of English composition. The performance of the part-of-speech tagging system directly affects the performance of the marking and analysis of the correction system. Therefore, this paper proposes an automatic scoring model for English composition based on article part-of-speech tagging. First, use the convolutional neural network to extract the word information from the character level and use this part of the information in the coarse-grained learning layer. Secondly, the word-level vector is introduced, and the residual network is used to establish an information path to integrate the coarse-grained annotation and word vector information. Then, the model relies on the recurrent neural network to extract the overall information of the sequence data to obtain accurate annotation results. Then, the features of the text content are extracted, and the automatic scoring model of English composition is constructed by means of model fusion. Finally, this paper uses the English composition scoring competition data set on the international data mining competition platform Kaggle to verify the effect of the model.

中文翻译：

---

面向词性标注的英语作文自动综合评分模型

英语作文的词性标注是自动纠正英语作文的基础。词性标记系统的性能直接影响标记和校正系统分析的性能。因此，本文提出了一种基于文章词性标注的英语作文自动评分模型。首先，使用卷积神经网络从字符级别提取单词信息，并在粗粒度学习层中使用这部分信息。其次，引入词级向量，利用残差网络建立一条信息路径，将粗粒度注释和词向量信息进行集成。然后，该模型依靠递归神经网络提取序列数据的整体信息，以获得准确的注释结果。然后，提取文本内容的特征，并通过模型融合构建英语写作的自动评分模型。最后，本文使用国际数据挖掘竞赛平台Kaggle上的英语作文评分竞赛数据集来验证模型的效果。