A learning-based framework to utilize E-HowNet ontology and Wikipedia sources to generate multiple-choice factual questions
|A learning-based framework to utilize E-HowNet ontology and Wikipedia sources to generate multiple-choice factual questions|
|Author(s)||Chu M.-H., Chen W.-Y., Lin S.-D.|
|Published in||Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012|
|Keyword(s)||Distractor, E-HowNet, Multiple-choice questions, Ontology, Wikipedia (Extra: Distractor, E-HowNet, Factual knowledge, Learning-based approach, Multiple-choice questions, Proficiency tests, Query terms, Subtasks, User study, Wikipedia, Artificial intelligence, Ontology, Query processing, Websites)|
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A learning-based framework to utilize E-HowNet ontology and Wikipedia sources to generate multiple-choice factual questions is a 2012 conference paper written in English by Chu M.-H., Chen W.-Y., Lin S.-D. and published in Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012.
This paper proposes a framework that automatically generates multiple-choice questions. Unlike most other similar works that focus on generating questions for English proficiency tests, this paper provides a framework to generate factual questions in Chinese. We have decomposed this problem into several sub-tasks: a) the identification of sentences that contain factual knowledge, b) the identification of the query term from each factual sentence, and c) the generation of distractors. Learning-based approaches are applied to address the first two problems. We then propose a way to generate distractors by using E-HowNet ontology database and Wikipedia sources. The system was evaluated through user study and test theory, and achieved a satisfaction rate of up to 70.6%.
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