Hybrid Ontology-Based Information Extraction for Automated Text Grading

Although automatic text grading systems have reached an accuracy level comparable to human grading, with successful commercial and research implementations (e.g., Latent Semantic Analysis), these systems can provide limited feedback about which statements of the text are incorrect and why they are incorrect. In the present work, we propose the use of a hybrid Ontology-based Information Extraction (OBIE) system to identify both correct and incorrect statements by combining extraction rules and machine learning based information extractors. Experiments show that given 77 student answers to a Cell Biology final exam question, our hybrid system can identify both correct and incorrect statements with high precision and recall measures.