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Combining Shallow and Linguistically Motivated Features in Native Language Identification., , , and . BEA@NAACL-HLT, page 197-206. The Association for Computer Linguistics, (2013)978-1-937284-47-3.Machine Learning and Applied Linguistics.. CoRR, (2018)A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity., , and . CoRR, (2016)Readability Classification for German using Lexical, Syntactic, and Morphological Features., , and . COLING, page 1063-1080. Indian Institute of Technology Bombay, (2012)A study of N-gram and Embedding Representations for Native Language Identification., and . BEA@EMNLP, page 240-248. Association for Computational Linguistics, (2017)Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts., , , and . CL4LC@COLING 2016, page 38-48. The COLING 2016 Organizing Committee, (2016)Role of Morpho-Syntactic Features in Estonian Proficiency Classification., and . BEA@NAACL-HLT, page 63-72. The Association for Computer Linguistics, (2013)978-1-937284-47-3.Experiments with Universal CEFR Classification., and . BEA@NAACL-HLT, page 147-153. Association for Computational Linguistics, (2018)Automated assessment of non-native learner essays: Investigating the role of linguistic features.. CoRR, (2016)Readability-based Sentence Ranking for Evaluating Text Simplification., and . CoRR, (2016)