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.

The DEMOCRAT project aims to develop linguistic research on French and in particular issues of text structuring through a detailed and contrastive analysis of reference chains (successive references to the same entity) in a varied corpus of texts covering the entire history of written French (9th-21st centuries). The project will make available to the scientific community: (i) an integrated and discursive model of referring and reference chains, (ii) an annotated corpus that can be used as a reference corpus as well as a training corpus for international evaluation campaigns on coreference, (iii) a tool for manual annotation, computer-aided annotation and annotated data management, and (iv) a system for the automatic identification of coreferences. The corpus that will be annotated in reference chains will be one million words long, i.e. about 100 000 annotated units. Motivations: (i) the need for an integrated model of referring expressions that would allow the modelling of reference chains and that is all the more precise from a linguistic point of view and formal enough to allow computational applications, (ii) the need for attested linguistic data, diachronic in particular, that allow on the one hand to appreciate the variations in chains composition, and on the other hand to serve as a reference corpus for the French language, on semantic data and not only morphologic or syntactic data, (iii) the need for an unified platform for corpus management, from visualization to querying and statistic computing, including annotation of phenomena from various linguistic dimensions, and (iv) the need for a natural language processing tool for the identification of reference chains for the French language. Model and corpus. In spite we can find a lot of existing descriptions of referring expressions, there does not exist any integrated description for coreference chains modelling. There does not exist either any prediction on their typologies or on their textual behavior. Moreover, there is no corpus allowing an assessment of the historical development of their composition. There is no corpus either allowing a comparison of their modes of cross-linguistic composition. There exists one corpus with anaphora annotations (ANCOR), for oral French, but there is no annotated corpus available for written French, i.e. implying long reference chains. Thus, the project aims at collecting a working corpus, relevant and diversified enough to account for the varied compositional modes of reference chains, and providing theoretical hypotheses on the notion of reference chains. These hypotheses should permit an annotation of the documents. It should also facilitate the improvement of existing annotation tools. Copyright free databases of annotated texts of Old French will be used and enriched: Corpus Représentatif des Premiers Textes Français, the Base de Français Mediéval and the Syntactic Reference Corpus of Medieval French. For contemporary French, we will exploit extracts from the ANR ORFEO corpus. Tool. The design and implementation of an annotation software platform, based on TXM platform, and enriched with ANALEC’s dynamic annotation functionalities, will lead to the proposition of a new and unified framework for efficient and ergonomic annotation and for launching experiments on computer-aided annotation. NLP system. To get a system for the automatic identification of reference chains, we will on the one hand use and optimize CROC (Coreference Resolution for Oral Corpus), a prototype designed and implemented at LATTICE using machine learning techniques, and on the other hand explore the design of hybrid systems, grouping several kinds of machine learning techniques and knowledge-based rules such as the ones from RefGen, a tool designed at LILPA. Then, DEMOCRAT will provide the first NLP system dedicated to the automatic detection of coreference chains for the French language. This system will participate to international campaigns.

This project aims at providing supplementary knowledge on stuttering, in order to help with its diagnostic and speech therapies. The project involves a multidisciplinary team including researchers in linguistics, computer science and neurology on the one hand, and, on the other hand, speech-language therapists specialized in stuttering. Members of the BENEPHIDIRE project are divided into 5 work-packages. The first work-package (henceforth WP) concerns mainly the coordination questions, while the second one aims at working on one of the neurological markers of stuttering, namely the frontal aslant tract (FAT). The purpose of this second WP is to verify, among children and adults, if the integrity this structure, as well as its connectivity, can be considered as an evidence of the stuttering severity and the risks of stuttering becoming chronic. As for the third and the fourth WPs, they aim at studying acoustic and motor characteristics of stuttering-like disfluencies, using audio and articulatory data. Finally, the fifth WP will realize a feasibility study on automatic identification of disfluencies, in order to help with developing, in the long run, an application allowing persons who stutter to auto-evaluate their fluency and to train their elocution. This project of a duration of 42 months fills in the gaps in the French scientific research on stuttering, given that the majority of studies on stuttering were lead in English. The originality of this project is based on several criteria: first, the neurological aspects studied in the project were not studied in many researches. The coupling of acoustic and motor information in order to identify the stuttering-like disfluencies is pioneering as well. In the short term, the ambition of BENEPHIDIRE is to provide supplementary knowledge on spatial and temporal aspects of stuttered speech. Moreover, our project will improve the diagnostic of stuttering, especially in children. Finally, this work will bring new perspectives as in the stuttering's definition as speech therapy.

Combating educational failure through innovative training applications is essential, especially for people with special needs. ALECTOR attempts to meet this challenge: to leverage document accessibility by developing assistive reading technologies for children with dyslexia and poor-readers. For these children, text simplification might be a powerful and possibly the only way to leverage document accessibility. The idea is not to impoverish written language, but to propose adapted versions of original documents that convey the same meaning, as a result of automatic linguistic transformations. The scientific issues addressed in this project include (a) readability assessment (identifying the complex elements of a text that make it hard to understand and read at an optimal speed), (b) lexical simplification (replacing the complex lexical units detected with simpler equivalents, while dealing with polysemy and multiwords expressions), (c) syntactic simplification (replacing complex sentences with shorter ones, avoiding subordinations, passive voices, parenthetical explanations, etc.), and (d) discourse transformations preserving text cohesion (replacing pronouns with their antecedents). The main innovative aspect is that the final tool will be tailored to poor and dyslexic readers: text transformations will be based on theoretical findings about the reading process and further refined by specific adaptations leveraging feedback from the targeted audience. As one of the key innovative deliverables, ALECTOR will propose a web-based application where simplified corpora will be available to teachers and speech therapists. The project brings together four complementary partners with substantial competence and expertise both in natural language processing and language impaired readers (dyslexic children): the Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur (LIMSI, Université Paris XI), the Linguistique, Langues et Parole group (LiLPa, Université de Strasbourg), the Laboratoire de Psychologie Cognitive (LPC, Aix Marseille Université) and the Laboratoire d’Informatique Fondamentale (LIF, Aix Marseille Université), which leads the consortium. An asset of the project will be the collaboration with the CENTAL (centre for NLP in Louvain-la-Neuve, Belgium) having a strong expertise in readability and text simplification.

Digital resources such as lexicons, dictionaries and text corpora, both raw and enriched with linguistic annotations, play a key role in enabling better inclusion of regional and minority languages in the digital world. Yet the gap between languages with many resources (fewer than ten languages) and ‘low resource’ languages remains wide. This gap is also documented in France, where regional languages are found to be very poorly equipped with digital resources and tools, compared to French. In this project, we will focus on four low resource languages of France: Alsatian, Corsican, Occitan and Poitevin-Saintongeais. From a theoretical point of view, the project will integrate and reassess existing linguistic knowledge about these languages, in comparison to other related languages. The goal will be to produce comprehensive and up-to-date descriptions, which can be captured in annotation guidelines. The project also aims to increase awareness about regional languages of France in the linguistics and computational linguistics research communities, by augmenting, collecting and building much needed unlabelled and labelled datasets. The corpora will integrate genres that approach or transcribe the oral language, e.g., theatre plays or narrative ethnotexts, as well as parallel translated documents. The labelled datasets will take the form of Universal Dependencies (UD) corpora. The use of the UD framework is motivated by its large adoption by the natural language processing community and the many tools and guidelines already available. Finally, the project will investigate how to share and transfer experience and tools between languages. This should enable those languages which are less advanced to be pulled upwards and thus benefit from the experience of others to accelerate their development. Beyond the concrete and immediate achievements for the languages represented in this project, the aim is also to build methodologies that can be used and applied to other less-resourced languages. This is also a means to build a community of researchers who work on less-resourced languages of France and neighbouring regions.