Application and research of English composition tangent model based on unsupervised semantic space

Abstract

Abstract Nowadays, major enterprises and schools vigorously promote the combination of information technology and subject teaching, among which automatic grading technology is more widely used. In order to improve the efficiency of English composition correction, the study proposes an unsupervised semantic space model for English composition tangent, using a Hierarchical Topic Tree Hybrid Semantic Space to achieve topic representation and clustering in English composition; adopts a feature dimensionality reduction method to select a set of semantic features to complete the optimization of the feature semantic space; and combines the tangent analysis algorithm to achieve intelligent scoring of English composition. The experimental data show that the accuracy and F-value of the English composition tangent analysis method based on the semantic space are significantly improved, and the Pearson correlation coefficient between the unsupervised semantic space English composition tangent model and the teacher’s manual grading is 0.8936. The results show that the unsupervised semantic space English composition tangent model has a higher accuracy rate, is more applicable, and can efficiently complete the English composition grading task: essay review task.