An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It the of automatic correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful time-consuming for teachers. In this paper, we present an NLP-based system MCQG Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words given lesson material. To validate not perverse, five materials were check effectiveness efficiency system. The extracted by teacher compared auto-generated result shows was capable extracting setting examinable questions. This outcome presented user-friendly interface easy accessibility.