International audience; En guise de postface, il nous a semblé nécessaire de revenir sur le processus collaboratif de la fabrication de cet ouvrage et de vous confier la genèse de ce projet. Tout est parti d'un constat pragmatique, de nos situations quotidiennes de travail : le/la chercheur·e qui produit ou utilise des données a besoin de réponses concrètes aux questions auxquelles il/elle est confronté·e sur son terrain comme lors de tous ses travaux de recherche. Produire, exploiter, diffuser, partager ou éditer des sources numériques fait aujourd'hui partie de notre travail ordinaire. La rupture apportée par le développement du web et l'arrivée du format numérique ont largement facilité la diffusion et le partage des ressources (documentaires, textuelles, photographiques, sonores ou audiovisuelles...) dans le monde de la recherche et, au-delà, auprès des citoyens de plus en plus curieux et intéressés par les documents produits par les scientifiques.
Publisher: Japanese Association for Digital Humanities
Project: EC | HIRMEOS (731102)
International audience; This paper presents an attempt to provide a generic named-entity recognition and disambiguation module (NERD) called entity-fishing as a stable online service that demonstrates the possible delivery of sustainable technical services within DARIAH, the European digital research infrastructure for the arts and humanities. Deployed as part of the national infrastructure Huma-Num in France, this service provides an efficient state-of-the-art implementation coupled with standardised interfaces allowing an easy deployment on a variety of potential digital humanities contexts. The topics of accessibility and sustainability have been long discussed in the attempt of providing some best practices in the widely fragmented ecosystem of the DARIAH research infrastructure. The history of entity-fishing has been mentioned as an example of good practice: initially developed in the context of the FP9 CENDARI, the project was well received by the user community and continued to be further developed within the H2020 HIRMEOS project where several open access publishers have integrated the service to their collections of published monographs as a means to enhance retrieval and access.entity-fishing implements entity extraction as well as disambiguation against Wikipedia and Wikidata entries. The service is accessible through a REST API which allows easier and seamless integration, language independent and stable convention and a widely used service oriented architecture (SOA) design. Input and output data are carried out over a query data model with a defined structure providing flexibility to support the processing of partially annotated text or the repartition of text over several queries. The interface implements a variety of functionalities, like language recognition, sentence segmentation and modules for accessing and looking up concepts in the knowledge base. The API itself integrates more advanced contextual parametrisation or ranked outputs, allowing for the resilient integration in various possible use cases. The entity-fishing API has been used as a concrete use case3 to draft the experimental stand-off proposal, which has been submitted for integration into the TEI guidelines. The representation is also compliant with the Web Annotation Data Model (WADM).In this paper we aim at describing the functionalities of the service as a reference contribution to the subject of web-based NERD services. In order to cover all aspects, the architecture is structured to provide two complementary viewpoints. First, we discuss the system from the data angle, detailing the workflow from input to output and unpacking each building box in the processing flow. Secondly, with a more academic approach, we provide a transversal schema of the different components taking into account non-functional requirements in order to facilitate the discovery of bottlenecks, hotspots and weaknesses. The attempt here is to give a description of the tool and, at the same time, a technical software engineering analysis which will help the reader to understand our choice for the resources allocated in the infrastructure.Thanks to the work of million of volunteers, Wikipedia has reached today stability and completeness that leave no usable alternatives on the market (considering also the licence aspect). The launch of Wikidata in 2010 have completed the picture with a complementary language independent meta-model which is becoming the scientific reference for many disciplines. After providing an introduction to Wikipedia and Wikidata, we describe the knowledge base: the data organisation, the entity-fishing process to exploit it and the way it is built from nightly dumps using an offline process.We conclude the paper by presenting our solution for the service deployment: how and which the resources where allocated. The service has been in production since Q3 of 2017, and extensively used by the H2020 HIRMEOS partners during the integration with the publishing platforms. We believe we have strived to provide the best performances with the minimum amount of resources. Thanks to the Huma-num infrastructure we still have the possibility to scale up the infrastructure as needed, for example to support an increase of demand or temporary needs to process huge backlog of documents. On the long term, thanks to this sustainable environment, we are planning to keep delivering the service far beyond the end of the H2020 HIRMEOS project.
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.
International audience; In recent years, a variety of initiatives have been funded with the aim of producing software tools or environments of a type variously known as virtual research environments, research infrastructures, or cyberinfrastructures. These initiatives vary in their scale, specialization, scope, and level of funding. One issue that they face in common, however, is that of sustainability: how can the continued--and useful--existence of a system or tool be guaranteed, or at least facilitated, once a project's funding has been spent? In this paper, we examine how such sustainability has been enabled, in the particular case of infrastructures for textual scholarship, in the context of three international projects: TextGrid,1 TEXTvre,2 and DARIAH3. Firstly, we will address the inter-project collaboration and crossfertilization between TextGrid and TEXTvre, including architectural decisions and shared data infrastructures, and investigate how the projects benefited from the exchange. We will then discuss how this existing collaboration can be taken forward by the loosely-coupled and distributed framework being developed by the DARIAH community, and how it can serve as a model for the sort of collaborations that DARIAH plans to enable.
International audience; This paper explores what is needed to foster an acceptance of digital practices in the humanities beyond the creation of pure infrastructure, specifically in terms of understanding and technically modelling traditional scholarly research within a digital medium while enabling new modes of scholarly work that could only be carried out within a digitally-mediated environment.
International audience; Defining digital humanities might be an endless debate if we stick to the discussion about the boundaries of this concept as an academic “discipline”. In an attempt to concretely identify this field and its actors, this paper shows that it is possible to analyse them through Twitter, a social media widely used by this “community of practice”. Based on a network analysis of 2,500 users identified as members of this movement, the visualisation of the “who’s following who?” graph allows us to highlight the structure of the network’s relationships, and identify users whose position is particular. Specifically, we show that linguistic groups are key factors to explain clustering within a network whose characteristics look similar to a small world.