Design of Automatic Scoring System for English Reading Comprehension: Based on Natural Language Processing Algorithm

Abstract

This paper presents the design and implementation of an Automatic Scoring System (ASS) for assessing English reading comprehension. Leveraging advancements in Natural Language Processing (NLP) algorithms, the system aims to provide an objective and efficient means of evaluating reading comprehension skills. The proposed system utilizes state-of-the-art NLP techniques to analyze textual input, extract key information, and assess the comprehension level of the reader. The development process involves several key components, including text preprocessing, feature extraction, and scoring algorithm design. Text preprocessing techniques such as tokenization, stemming, and stop-word removal are applied to enhance the quality of textual input. Feature extraction methods capture relevant linguistic features from the text, including vocabulary richness, syntactic complexity, and coherence. These features are then utilized by the scoring algorithm to generate accurate assessments of reading comprehension proficiency. The system is designed to accommodate various types of reading materials, ranging from short passages to longer texts, and can adapt to different difficulty levels. Experimental results demonstrate the effectiveness of the proposed system in accurately assessing reading comprehension skills across a diverse range of texts. The ASS holds promise as a valuable tool for educators, researchers, and language learners seeking objective and timely feedback on reading comprehension performance.