Subjective Answer Evaluation for Technical Subjects with NLP-Driven Advancements

This research paper addresses the challenge of manually grading subjective papers. It examines existing methods and suggests a new approach using computers and advanced tools to enhance the speed and accuracy of grading. Through experimentation, the study assesses the performance of this novel method. The results contribute to automated grading systems for subjective papers. Additionally, this paper proposes a novel approach utilizing natural language processing (NLP) techniques, including WordNet, cosine similarity, and TF-IDF, to automatically assess descriptive answers.