The heterogeneity of levels of langage learners is very frequent in the same class and its handle represents a major problem for the langage teachers, which should provide personalised resources to each learner. Thus, the STAR-FLE project aims to propose innovant digital solutions available in the Natural Language Processing (NLP) area, that may improve text comprehension of French L2 learners and that helps teachers to handle multiple levels of learners. We proposed context-based aided for the comprehension of lexical issues, but also of MWE expressions found in original texts. Our system provides MWE identification, generation of definitions adressed to a specific learner’s profile but also synonym search, word sense disambiguation and simpler synomyms and the possibility to chose simpler synonyms for a better comprehension of a text. On the other hand, we build original NLP resources such as annotated CEFR corpus and lexicons, MWE annotated corpus.