An improved method for extracting essay tangency features in intelligent scoring of English essays

Abstract

With the continuous improvement of teaching quality in China's colleges and universities, English teaching has received more and more attention, and the automatic scoring system for English composition has begun to be gradually applied in English teaching in colleges and universities, but the system can only score compositions objectively, which is difficult to meet the actual teaching needs. Therefore, this study first proposes a topic richness based approach for analyzing composition cut points, and then proposes a combination based automatic grading method for English compositions. Based on the above, an intelligent grading improvement method based on composition tangent feature extraction is proposed to improve the efficiency of the English composition grading system and reduce the teaching burden on teachers. The traditional grading method is to grade grammar or essays by introducing Analytic Hierarchy Process, artificial neural networks, or combining machine learning theory with multi feature fusion. However, based on the characteristics of single semantic similarity and keyword alignment, the comprehensiveness of the grading is insufficient, making it difficult to analyze the correlation between essay sentences and the overall theme. The experimental results show that the topic decision-making model proposed in this study has better deep learning performance, and its scoring accuracy shows an upward trend with the increase of the sample data size of the essay, increasing from 40.05% to 91.46%. And the main content of the learned is the irrelevant information of the first sentence of a new topic and the truncation point, which can effectively distinguish whether a sentence is a topic segmentation point, and effectively identifies whether two sentences belong to the same topic; the proposed topic segmentation model has better practicality and its ability to segment topics is stronger; the combined method proposed in this study The overall performance of the model is better than the composition scoring methods based on RNN and BiLSTM models. Under the same semantic vectorization processing operation, the per values present in the composition sets are all different, and this result indicates that the writers' ability and the difficulty of writing on the topics are all different. It is hoped that this study can provide some solution ideas for the research of intelligent scoring feature extraction methods in English.