“Automatic generation of cloze question stems,”
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Machine learning has been used to classify whether a certain sentence is suitable to be used as a stem in cloze questions.
“Machine learning has also been used in such contexts, e.g., to classify whether a certain sentence is suitable to be used as a stem in cloze questions (passage with a portion occluded which needs to be replaced by the participant) (Correia et al., 2012).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Correia et al., 2012
Cites
Kilgarriff, A. (2000) · Language · doi:10.2307/417141 · Cited by 11,595 · see citing works
(1998) · The MIT Press eBooks · doi:10.7551/mitpress/7287.001.0001 · Cited by 4,849 · see citing works
TextRank: Bringing Order into Text
Mihalcea, R. F., & Tarau, P. (2004) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · Cited by 3,631 · see citing works
Gospodnetić, O., Hatcher, E., & Cutting, D. (2005) · No Category · Cited by 610 · see citing works
Classroom success of an intelligent tutoring system for lexical practice and reading comprehension
Heilman, M., Collins‐Thompson, K., Callan, J., & Eskénazi, M. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-282 · Cited by 70 · see citing works
A real-time multiple-choice question generation for language testing
Hoshino, A., & Nakagawa, H. (2005) · doi:10.3115/1609829.1609832 · Cited by 66 · see citing works
Evaluating CETEMPúblico, a free resource for Portuguese
Santos, D., & Rocha, P. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073012.1073070 · Cited by 51 · see citing works
A Multi-Input Dependency Parser
Aı̈t-Mokhtar, S., Chanod, J.-P., & Roux, C. (2001) · International Workshop/Conference on Parsing Technologies · Cited by 41 · see citing works
Using Morphossyntactic Information in TTS Systems: Comparing Strategies for European Portuguese
Ribeiro, R., Oliveira, L., & Trancoso, I. M. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45011-4_21 · Cited by 31 · see citing works
P-AWL: Academic Word List for Portuguese
Baptista, J., Costa, N., Guerra, J., Zampieri, M., Cabral, M., & Mamede, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12320-7_15 · Cited by 26 · see citing works
Self-citationPorting REAP to European Portuguese
Marujo, L., Lopes, J., Mamede, N., Trancoso, I. M., Pino, J., Eskénazi, M., Baptista, J., & Viana, C. (2009) · ISCA Workshop on Speech and Language Technology in Education (SLaTE) · doi:10.21437/slate.2009-28 · Cited by 23 · see citing works
Self-citationPredicting Cloze Task Quality for Vocabulary Training
Skory, A., & Eskénazi, M. (2010) · Cited by 19 · see citing works
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CH, D. R., & Saha, S. K. (2020) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2018.2889100 · Cited by 144 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
Automatic question generation and answer assessment: a survey
Das, B., Majumder, M., Phadikar, S., & Sekh, A. A. (2021) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-021-00151-1 · Cited by 123 · see citing works
Large-scale Cloze Test Dataset Created by Teachers
Xie, Q., Lai, G., Dai, Z., & Hovy, E. H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1257 · Cited by 119 · see citing works
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection
Majumder, M., & Saha, S. K. (2015) · Workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA) · doi:10.18653/v1/w15-4410 · Cited by 54 · see citing works
Kumar, A. P., Nayak, A., K, M. S., Chaitanya, & Ghosh, K. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00333-6 · Cited by 41 · see citing works
Maheen, F., Asif, M., Ahmad, H., Ahmad, S., Alturise, F., Asiry, O., & Ghadi, Y. Y. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1010 · Cited by 34 · see citing works
Factual open cloze question generation for assessment of learner’s knowledge
Das, B., & Majumder, M. (2017) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-017-0060-3 · Cited by 32 · see citing works
Majumder, M., & Saha, S. K. (2014) · Knowledge Management & E-Learning An International Journal · doi:10.34105/j.kmel.2014.06.025 · Cited by 24 · see citing works
A comprehensive review on MCQ generation from text
Madri, V. R., & Meruva, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14768-5 · Cited by 15 · see citing works
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Oliveira, H. G. (2018) · Information · doi:10.3390/info9020034 · Cited by 14 · see citing works
Onto.PT: Towards the Automatic Construction of a Lexical Ontology for Portuguese
Oliveira, H. G. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 12 · see citing works
Online investigation of users’ attitudes using automatic question answering
Zhang, C., & Zhou, Q. (2018) · Online Information Review · doi:10.1108/oir-10-2016-0299 · Cited by 11 · see citing works
AiXAM - AI assisted Online MCQ Generation Platform using Google T5 and Sense2Vec
Dhanya, N. M., Balaji, R. K., & Akash, S. (2022) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais53314.2022.9743027 · Cited by 11 · see citing works
Geração Automática de Questões a Partir do Reconhecimento de Entidades Nomeadas em Textos Didáticos
Pirovani, J. C. M., Spalenza, M. A., & de Oliveira, E. S. (2017) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/cbie.sbie.2017.1147 · Cited by 11 · see citing works
Factual Question Generation for the Portuguese Language
Leite, B., Cardoso, H. L., Reis, L. P., & Soares, C. (2020) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista49547.2020.9194631 · Cited by 9 · see citing works
Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers
Felice, M., Taslimipoor, S., & Buttery, P. J. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.100 · Cited by 7 · see citing works
Oliveira, H. G., Coelho, I. E., & Gomes, P. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3x3y4c7s6vap · Cited by 5 · see citing works
Agyemang, F. G., Boateng, H., & Dzandu, M. D. (2017) · Knowledge Management & E-Learning An International Journal · doi:10.34105/j.kmel.2017.09.030 · Cited by 5 · see citing works
Generating Adequate Distractors for Multiple-Choice Questions
Zhang, C., Sun, Y., Chen, H., & Wang, J. (2020) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0010148303100315 · Cited by 4 · see citing works
Neural Question Generation for the Portuguese Language: A Preliminary Study
Leite, B., & Cardoso, H. L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16474-3_63 · Cited by 4 · see citing works
Answering Fill-in-the-Blank Questions in Portuguese with Transformer Language Models
Oliveira, H. G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86230-5_58 · Cited by 3 · see citing works
The development and validation of a C-test and a pseudo-character test for online CFL assessment
Yang, X., & Osborne, C. (2023) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2022-0019 · Cited by 3 · see citing works
Cloze Quality Estimation for Language Assessment
Zhang, Z., Mita, M., & Komachi, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.39 · Cited by 4 · see citing works
Evaluating Semantic Similarity Methods to Build Semantic Predictability Norms of Reading Data
Leal, S. E., Casanova, E., Paetzold, G. H., & Aluísio, S. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83527-9_3 · Cited by 3 · see citing works
Cloze Quality Estimation for Language Assessment
Zhang, Z., Mita, M., & Komachi, M. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.328 · Cited by 2 · see citing works
ClozEx: A Task toward Generation of English Cloze Explanation
Zhang, Z., Mita, M., & Komachi, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.347 · Cited by 2 · see citing works
Using Learning Analytics for Adaptive Exercise Generation
Heck, T., & Meurers, D. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.4 · Cited by 1 · see citing works
Anwar, M. R., Khalid, S., Alshahrani, S., Bilal, H. S. M., & Aldawsari, M. F. (2026) · IEEE Access · doi:10.1109/access.2026.3652006 · Cited by 1 · see citing works
Automatic Generation System of True or False Questions
Eom, J.-S., Lim, U.-K., & Lee, J.-Y. (2016) · Advanced science and technology letters · doi:10.14257/astl.2016.135.12 · Cited by 1 · see citing works
Classificador de textos para o ensino de portugues como segunda l´õngua
dos Santos, P., & Curto, L. (2014) · Cited by 1 · see citing works
On the Utility of Portuguese Term-Based Lexical-Semantic Networks
Oliveira, H. G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09761-9_18 · Cited by 1 · see citing works
Leal, S. E. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2021.tde-16072021-115303
Shimmei, M., Uto, M., Matsubayashi, Y., Inui, K., Mallavarapu, A., & Matsuda, N. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99264-3_16
- Authors
- Correia, R., Baptista, J., Eskenazi, M., & Mamede, N.
- Affiliations
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- University of Algarve (PT)
- Venue
- Lecture notes in computer science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 168-178
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W163271066
- Cited by
- 35
- References
- 12
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 7 · 2022: 4 · 2021: 4 · 2020: 2 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 2 · 2014: 4 · 2012: 1
- FWCI
- 1.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- cloze questions, fill-in-the-blank questions, European Portuguese, morphologically rich languages, tutoring systems, supervised classification, feature engineering, data scarcity, personalized instruction
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