[
Bird, S., Klein, E., and Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. Sebastopol, CA: O’Reilly Media, Inc, 509 p.
]Search in Google Scholar
[
Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794, San Francisco, CA. Accessible at: http://doi.acm.org/10.1145/2939672.2939785.
]Search in Google Scholar
[
Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Accessible at: http://dx.doi.org/10.18653/v1/N19-1423.
]Search in Google Scholar
[
Dorogush, A., Ershov, V., and Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. ArXiV preprint, 7 p. Accessible at: https://arxiv.org/abs/1810.11363.
]Search in Google Scholar
[
Fares, M., Kutuzov, A., Oepen, S., and Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In Proceedings of the 21st Nordic Conference on Computational Linguistics, pages 271–276, Gothenburg, Sweden. Accessible at: https://aclanthology.org/W17-0237/.
]Search in Google Scholar
[
Francis, W., and Kucera, H. (1979). Brown Corpus. Providence, Rhode Island: Department of Linguistics, Brown University. Accessible at: http://korpus.uib.no/icame/manuals/BROWN/INDEX.HTM.
]Search in Google Scholar
[
Granger, S. (2008). Learner Corpora. In A. Lüdeling – M. Kyto (eds.): Corpus Linguistics. An International Handbook. Volume 1. Berlin: Walter de Gruyter, pages 259–275.
]Search in Google Scholar
[
Jiang, S., and Lee, J. (2017). Distractor Generation for Chinese Fill-in-the-blank Items. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 143–148, Copenhagen, Denmark. Accessible at: http://dx.doi.org/10.18653/v1/W17-5015.
]Search in Google Scholar
[
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st Internal Conference on Neural Information Processing Systems (NIPS 2017), pages 3149–3157, Long Beach, CA. Accessible at: https://dl.acm.org/doi/10.5555/3294996.3295074.
]Search in Google Scholar
[
Kumar, G., Banchs, R., and D’Haro, L. (2015). Automatic fill-the-blank question generator for student self-assessment. In Proceedings of 2015 IEEE Frontiers in Education Conference (FIE), pages 1–3, El Paso, TX. Accessible at: https://doi.org/10.1109/FIE.2015.7344291.
]Search in Google Scholar
[
Kurdi, S. (2020). A Systematic Review of Automatic Question Generation for Educational Purposes. International Journal of Artificial Intelligence in Education, 30(1), pages 121–204.
]Search in Google Scholar
[
Liu, M., Rus, V., and Liu, L. (2018). Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy. IEEE Transactions on Learning Technologies, 11(2), pages 193–202.
]Search in Google Scholar
[
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ArXiV preprint, 12 p. Accessible at: https://arxiv.org/abs/1301.3781.
]Search in Google Scholar
[
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, pages 2824–2830.
]Search in Google Scholar
[
Řehůřek, R., and Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 46–50, Malta. Accessible at: http://dx.doi.org/10.13140/2.1.2393.1847.
]Search in Google Scholar
[
Sakaguchi, K., Arase, Y., and Komachi, M. (2013). Discriminative Approach to Fill-inthe- Blank Quiz Generation for Language Learners. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 238–242, Sofia, Bulgaria. Accessible at: https://aclanthology.org/P13-2043/.
]Search in Google Scholar
[
Stenetorp, P., Pyysalo, S., Topic, G., Ohta, T., Ananiadou, S., and Tsujii, J. (2012). brat: a Web-based Tool for NLP-Assisted Text Annotation. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 102–107, Avignon, France. Accessible at: https://aclanthology.org/E12-2021/.
]Search in Google Scholar
[
Vinogradova, O. (2019). To automated generation of test questions on the basis of error annotations in EFL essays: A time-saving tool? In S. Götz – J. Mukherjee (eds.): Learner Corpora and Language Teaching. Volume 29. Amsterdam, Netherlands: John Benjamins, pages 29–48.
]Search in Google Scholar
[
Vinogradova, O., and Lyashevskaya, O. (2022). Review of Practices of Collecting and Annotating Texts in the Learner Corpus REALEC. In P. Sojka – A. Horák – I. Kopeček – K. Pala (eds.): Text, Speech and Dialogue. 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings. Cham, Switzerland: Springer Nature Switzerland AG, pages 77–88.
]Search in Google Scholar
