QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_11
Reference22 articles.
1. Deepak, G., Kumar, N., Bharadwaj, G.V.S.Y., Santhanavijayan, A.: OntoQuest: an ontological strategy for automatic question generation for e-assessment using static and dynamic knowledge. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), pp. 1–6. IEEE (December 2019)
2. Patra, R., Saha, S.K.: A hybrid approach for automatic generation of named entity distractors for multiple choice questions. Educ. Inf. Technol. 24(2), 973–993 (2018). https://doi.org/10.1007/s10639-018-9814-3
3. Dhanya, N.M., Balaji, R.K., Akash, S.: AiXAM-AI assisted online MCQ generation platform using google T5 and Sense2Vec. In: 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 38–44. IEEE (February 2022)
4. Agarwal, R., Negi, V., Kalra, A., Mittal, A.: Deep learning and linguistic feature based automatic multiple choice question generation from text. In: International Conference on Distributed Computing and Internet Technology, pp. 260–264. Springer, Cham (January 2022)
5. Hsiao, I.H., Chung, C.Y.: AI-infused semantic model to enrich and expand programming question generation. J. Artif. Intell. Technol. 2(2), 47–54 (2022)
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