An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques
| Autor: | Ikechukwu E. Onyenwe, Chidinma A. Nwafor |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language business.industry Computer science Interface (Java) Computer Science - Artificial Intelligence computer.software_genre Outcome (game theory) Computer-Based Test Examination Task (project management) Artificial Intelligence (cs.AI) Question generation Multiple-Choice Question Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing Natural Language Processing Multiple choice |
| Popis: | Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility. Recently accepted by the International Journal on Natural Language Computing (IJNLC) awaiting publication, 11 pages, 4 figures, 5 tables |
| Databáze: | OpenAIRE |
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