Article: Automatic scoring algorithm for English essays based on topic granularity segmentation and feature extraction Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.11 pp.151 - 175 Abstract: Automatic scoring of English essays is important for teaching evaluation and large-scale assessment, but existing methods often insufficiently capture discourse logic and multi-dimensional writing features. This study proposes an automatic scoring model based on topic granularity segmentation and feature extraction. First, a CNN-BiLSTM-CRF framework is constructed to segment essays into coherent topic units and capture topic development logic. Then, RoBERTa is used to extract semantic, lexical, syntactic, and structural features, which are integrated by LightGBM for final scoring. Experimental results show that the proposed model achieves a correlation coefficient of 0.9613 with human scoring, maintains scoring accuracy above 95.1% across different essay types, obtains an F1 score of 95.6%, precision of 94.8%, and a mean absolute error of 0.67. Its maximum memory usage is 394 MB, and the response time for 2,000 essays is 6.24 s, demonstrating high scoring accuracy and efficiency. Inderscience Publishers - linking academia, business and industry through research

Title: Automatic scoring algorithm for English essays based on topic granularity segmentation and feature extraction

Authors: Jia Chen; Yuanyuan Sun; Shumin Liu

Addresses: School of Foreign Languages, North China Institute of Aerospace Engineering, Langfang, 065000, China ' School of Foreign Languages, North China Institute of Aerospace Engineering, Langfang, 065000, China ' School of Materials Engineering, North China Institute of Aerospace Engineering, Langfang, 065000, China

Abstract: Automatic scoring of English essays is important for teaching evaluation and large-scale assessment, but existing methods often insufficiently capture discourse logic and multi-dimensional writing features. This study proposes an automatic scoring model based on topic granularity segmentation and feature extraction. First, a CNN-BiLSTM-CRF framework is constructed to segment essays into coherent topic units and capture topic development logic. Then, RoBERTa is used to extract semantic, lexical, syntactic, and structural features, which are integrated by LightGBM for final scoring. Experimental results show that the proposed model achieves a correlation coefficient of 0.9613 with human scoring, maintains scoring accuracy above 95.1% across different essay types, obtains an F1 score of 95.6%, precision of 94.8%, and a mean absolute error of 0.67. Its maximum memory usage is 394 MB, and the response time for 2,000 essays is 6.24 s, demonstrating high scoring accuracy and efficiency.

Keywords: topic granularity segmentation; feature extraction; English essays; automatic scoring; deep learning.

DOI: 10.1504/IJCEELL.2026.154673

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.11, pp.151 - 175

Received: 16 Sep 2025
Accepted: 23 Jan 2026
Published online: 09 Jul 2026 *

Title: Automatic scoring algorithm for English essays based on topic granularity segmentation and feature extraction

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