O. Biran and K. Mckeown, Human-Centric Justification of Machine Learning Predictions, Twenty-Sixth International Joint Conference on Artificial Intelligence, pp.1461-1467, 2017.

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, Journal of machine Learning research, vol.3, pp.993-1022, 2003.

V. Butoianu, P. Vidal, E. Duval, J. Broisin, and K. Verbert, User Context and Personalized Learning: a Federation of Contextualized Attention Metadata, Journal of Universal Computer Science, vol.16, issue.16, pp.2252-2271, 2010.

C. Cechinel, S. S. Alonso, M. Sicilia, and M. C. De-mattos, Descriptive Analysis of Learning Object Material Types in MERLOT, Research Conference on Metadata and Semantic Research, pp.331-341, 2010.

E. Dale and J. C. , English, and 1949. The concept of readability, Elementary English, vol.26, issue.1, pp.19-26, 1949.

M. Dascalu, P. Dessus, S. Trausan-matu, M. Bianco, and A. Nardy, ReaderBench, an Environment for Analyzing Text Complexity and Reading Strategies, International Conference on Artificial Intelligence in Education, pp.379-388, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00871568

M. Dascalu, G. Gutu, S. Ruseti, I. C. Paraschiv, P. Dessus et al., ReaderBench -A Multi-lingual Framework for Analyzing Text Complexity, European Conference on Technology Enhanced Learning, pp.495-499, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01584870

E. Davoodi and L. Kosseim, On the Contribution of Discourse Structure on Text Complexity Assessment. arXiv.org, 2017.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American society for information science, vol.41, issue.6, pp.391-407, 1990.

C. A. Denton, M. Enos, M. J. York, D. J. Francis, M. A. Barnes et al., Text-Processing Differences in Adolescent Adequate and Poor Comprehenders Reading Accessible and Challenging Narrative and Informational Text, Reading Research Quarterly, vol.50, issue.4, pp.393-416, 2015.

J. Falkenjack and A. Jönsson, Classifying easy-to-read texts without parsing, Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), pp.114-122, 2014.

J. Falkenjack, K. H. Mühlenbock, and A. J. , P. o. t. 19th, and 2013. Features indicating readability in Swedish text, Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), pp.27-40, 2013.

J. Geertzen, T. Alexopoulou, and A. Korhonen, Automatic linguistic annotation of large scale l2 databases: The ef-cambridge open language database (efcamdat), Proceedings of the 31st Second Language Research Forum, 2013.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010.

Q. Han and F. Gao, Towards semantic learning object metadata: mapping standard metadata specifications to ontologies, Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) 2012, pp.1-12, 2012.

Y. Huang, A. Murakami, T. Alexopoulou, and A. Korhonen, Dependency parsing of learner English, International Journal of Corpus Linguistics, vol.23, issue.1, pp.28-54, 2018.

Z. H. Kilimci and S. Akyokus, Deep Learning-and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification, Complexity, issue.7, pp.1-10, 2018.

W. Kintsch and T. A. , Van Dijk Psychological review, and 1978. Toward a model of text comprehension and production, Psychological review, vol.85, issue.5, p.363, 1978.

B. Kopainsky, P. P. Dummer, and S. M. Alessi, Automated assessment of learners' understanding in complex dynamic systems, System Dynamics Review, vol.28, issue.2, pp.131-156, 2012.

X. Lu, Automatic analysis of syntactic complexity in second language writing, International journal of corpus linguistics, vol.15, issue.4, pp.474-496, 2010.

X. Lu, Automated measurement of syntactic complexity in corpus-based l2 writing research and implications for writing assessment, Language Testing, vol.34, issue.4, pp.493-511, 2017.

P. N. Mendes, M. Jakob, A. García-silva, and C. Bizer, Dbpedia spotlight: shedding light on the web of documents, Proceedings of the 7th international conference on semantic systems, pp.1-8, 2011.

T. Mikolov, K. C. 0010, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, 2013.

D. Napolitano, K. Sheehan, and R. Mundkowsky, Online Readability and Text Complexity Analysis with TextEvaluator, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp.96-100, 2015.

S. Narayan and C. Gardent, Hybrid simplification using deep semantics and machine translation, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol.1, pp.435-445, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01109581

M. E. Newman, Assortative mixing in networks, Physical review letters, vol.89, issue.20, p.208701, 2002.

J. Pennington, R. Socher, and C. D. Manning, GloveGlobal Vectors for Word Representation, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014.

M. T. Ribeiro, S. Singh, and C. Guestrin, Why Should I Trust You?, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp.1135-1144, 2016.

A. Siddharthan, A survey of research on text simplification, ITL-International Journal of Applied Linguistics, vol.165, issue.2, pp.259-298, 2014.

S. ?tajner and I. Hulpus, Automatic assessment of conceptual text complexity using knowledge graphs, Proceedings of the 27th International Conference on Computational Linguistics, pp.318-330, 2018.