Automated Essay Scoring via NLP: System Architectures, Feature Engineering, and Evaluation Metrics

Abstract

With the continuous advancement of natural language processing (NLP) technology, automatic essay scoring systems have emerged as a prominent research focus in the field of education. This paper first introduces the fundamentals of NLP and its applications in text analysis, including part-of-speech tagging, named entity recognition, and text classification. It then elaborates on the architecture of automatic essay scoring systems, covering overall system design, preprocessing modules, feature extraction modules, and scoring model modules. Subsequently, it delves into evaluation methods for essay grading systems, including metric frameworks and experimental designs. This paper aims to provide comprehensive guidance for researchers in related fields, advancing the application of natural language processing in essay grading.