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The aim of our project was to offer a free, TEI-aware and TEI-friendly text editor, as a basic but viable alternative to commercial software for students learning to encode in TEI, and also for scholars with simple needs and low financial resources.

International audience; Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.

This report discusses the scholarly communication issues in Social Sciences and Humanities that are relevant to the future development and functioning of OPERAS. The outcomes collected here can be divided into two groups of innovations regarding 1) the operation of OPERAS, and 2) its activities. The “operational” issues include the ways in which an innovative research infrastructure should be governed (Chapter 1) as well as the business models for open access publications in Social Sciences and Humanities (Chapter 2). The other group of issues is dedicated to strategic areas where OPERAS and its services may play an instrumental role in providing, enabling, or unlocking innovation: FAIR data (Chapter 3), bibliodiversity and multilingualism in scholarly communication (Chapter 4), the future of scholarly writing (Chapter 5), and quality assessment (Chapter 6). Each chapter provides an overview of the main findings and challenges with emphasis on recommendations for OPERAS and other stakeholders like e-infrastructures, publishers, SSH researchers, research performing organisations, policy makers, and funders. Links to data and further publications stemming from work concerning particular tasks are located at the end of each chapter.

The present paper describes the etymological component of the TEI Lex-0 initiative which aims at defining a terser subset of the TEI guidelines for the representation of etymological features in dictionary entries. Going beyond the basic provision of etymological mechanisms in the TEI guidelines, TEI Lex-0 Etym proposes a systematic representation of etymological and cognate descriptions by means of embedded constructs based on the (for etymologies) and (for etymons and cognates) elements. In particular, given that all the potential contents of etymons are highly analogous to those of dictionary entries in general, the contents presented herein heavily re-use many of the corresponding features and constraints introduced in other components of the TEI Lex-0 to the encoding of etymologies and etymons. The TEI Lex-0 Etym model is also closely aligned to ISO 24613-3 on modelling etymological data and the corresponding TEI serialisation available in ISO 24613-4.

In this resource, you can follow a step-by-step description of a research data workflow involving the annotation of multilingual parliamentary corpora (French, German, British) according to the guidelines of the Text Encoding Initiative (TEI). Read further if you are interested in working with the TEI, analyzing parliamentary corpora, or simply would like to see a validated example of how FAIR and open data is implemented in the context of a PhD dissertation in Corpus Linguistics.

How can researchers identify suitable research data repositories for the deposit of their research data? Which repository matches best the technical and legal requirements of a specific research project? For this end and with a humanities perspective the Data Deposit Recommendation Service (DDRS) has been developed as a prototype. It not only serves as a functional service for selecting humanities research data repositories but it is particularly a technical demonstrator illustrating the potential of re-using an already existing infrastructure - in this case re3data - and the feasibility to set up this kind of service for other research disciplines. The documentation and the code of this project can be found in the DARIAH GitHub repository: ​https://dariah-eric.github.io/ddrs/​.

Dictionaries could be considered as the most comprehensive reservoir of human knowledge, which carry not only the lexical description of words in one or more languages, but also the common awareness of a certain communityabout every known piece of knowledge in a time frame. Print dictionaries are the principle resources which enable the documentation and transfer of such knowledge. They already exist in abundant numbers, while new onesare continuously compiled, even with the recent strong move to digital resources.However, a majority of these dictionaries, even when available digitally, is still not fully structured due to the absence of scalable methods and techniques that can cover the variety of corresponding material. Moreover, the relatively few existing structured resources present limited exchange and query alternatives, given the discrepancy of their data models and formats.In this thesis we address the task of parsing lexical information in print dictionaries through the design of computer models that enable their automatic structuring. Solving this task goes hand in hand with finding a standardised output for these models to guarantee a maximum interoperability among resources and usability for downstream tasks.First, we present different classifications of the dictionaric resources to delimit the category of print dictionaries we aim to process. Second, we introduce the parsing task by providing an overview of the processing challengesand a study of the state of the art. Then, we present a novel approach based on a top-down parsing of the lexical information. We also outline the archiecture of the resulting system, called GROBID-Dictionaries, and the methodology we followed to close the gap between the conception of the system and its applicability to real-world scenarios.After that, we draw the landscape of the leading standards for structured lexical resources. In addition, we provide an analysis of two ongoing initiatives, TEI-Lex-0 and LMF, that aim at the unification of modelling the lexical information in print and electronic dictionaries. Based on that, we present a serialisation format that is inline with the schemes of the two standardisation initiatives and fits the approach implemented in our parsing system.After presenting the parsing and standardised serialisation facets of our lexical models, we provide an empirical study of their performance and behaviour. The investigation is based on a specific machine learning setup andseries of experiments carried out with a selected pool of varied dictionaries.We try in this study to present different ways for feature engineering and exhibit the strength and the limits of the best resulting models. We also dedicate two series of experiments for exploring the scalability of our models with regard to the processed documents and the employed machine learning technique.Finally, we sum up this thesis by presenting the major conclusions and opening new perspectives for extending our investigations in a number of research directions for parsing entry-based documents.; Les dictionnaires peuvent être considérés comme le réservoir le plus compréhensible de connaissances humaines, qui contiennent non seulement la description lexicale des mots dans une ou plusieurs langues, mais aussi la conscience commune d’une certaine communauté sur chaque élément de connaissance connu dans une période de temps donnée. Les dictionnaires imprimés sont les principales ressources qui permettent la documentation et le transfert de ces connaissances. Ils existent déjà en grand nombre, et de nouveaux dictionnaires sont continuellement compilés.Cependant, la majorité de ces dictionnaires dans leur version numérique n’est toujours pas structurée en raison de l’absence de méthodes et de techniques évolutives pouvant couvrir le nombre du matériel croissant et sa variété. En outre, les ressources structurées existantes, relativement peu nombreuses, présentent des alternatives d’échange et de recherche limitées, en raison d’un sérieux manque de synchronisation entre leurs schémas de structure.Dans cette thèse, nous abordons la tâche d’analyse des informations lexicales dans les dictionnaires imprimés en construisant des modèles qui permettent leur structuration automatique. La résolution de cette tâche va depair avec la recherche d’une sortie standardisée de ces modèles afin de garantir une interopérabilité maximale entre les ressources et une facilité d’utilisation pour les tâches en aval.Nous commençons par présenter différentes classifications des ressources dictionnaires pour délimiter les catégories des dictionnaires imprimés sur lesquelles ce travail se focalise. Ensuite, nous définissions la tâche d’analyse en fournissant un aperçu des défis de traitement et une étude de l’état de l’art.Nous présentons par la suite une nouvelle approche basée sur une analyse en cascade de l’information lexicale. Nous décrivons également l’architecture du système résultant, appelé GROBID-Dictionaries, et la méthodologie quenous avons suivie pour rapprocher la conception du système de son applicabilité aux scénarios du monde réel.Ensuite, nous prestons des normes clés pour les ressources lexicales structurées. En outre, nous fournissons une analyse de deux initiatives en cours, TEI-Lex-0 et LMF, qui visent à unifier la modélisation de l’information lexicale dans les dictionnaires imprimés et électroniques. Sur cette base, nous présentons un format de sérialisation conforme aux schémas des deux initiatives de normalisation et qui est assorti à l’approche développée dans notresystème d’analyse lexicale.Après avoir présenté les facettes d’analyse et de sérialisation normalisées de nos modèles lexicaux, nous fournissons une étude empirique de leurs performances et de leurs comportements. L’étude est basée sur une configuration spécifique d’apprentissage automatique et sur une série d’expériences menées avec un ensemble sélectionné de dictionnaires variés. Dans cette étude, nous essayons de présenter différentes manières d’ingénierie des caractéristiques et de montrer les points forts et les limites des meilleurs modèles résultants. Nous consacrons également deux séries d’expériences pour explorer l’extensibilité de nos modèles en ce qui concerne les documents traités et la technique d’apprentissage automatique employée.Enfin, nous clôturons cette thèse en présentant les principales conclusions et en ouvrant de nouvelles perspectives pour l’extension de nos investigations dans un certain nombre de directions de recherche pour l’analyse des documents structurés en un ensemble d’entrées.

International audience; This article presents an overview of approaches and results during our participation in the CLEF HIPE 2020 NERC-COARSE-LIT and EL-ONLY tasks for English and French. For these two tasks, we use two systems: 1) DeLFT, a Deep Learning framework for text processing; 2) entity-fishing, generic named entity recognition and disambiguation service deployed in the technical framework of INRIA.