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This document presents the creation of a text classifier to predict the disciplinary affiliation of titles (from books or articles) in the humanities and social sciences. It implements ISIDORE and Keras API for Deep Learning.
", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2020-08-19", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "10.5281/zenodo.3991994", "relation_type": { "id": "issupplementedby", "title": { "de": "Wird erg\u00e4nzt durch", "en": "Is supplemented by" } }, "resource_type": { "id": "publication-workingpaper", "title": { "de": "Arbeitspapier", "en": "Working paper" } }, "scheme": "doi" }, { "identifier": "https://gitlab.huma-num.fr/spouyllau/isidore-jupyter", "relation_type": { "id": "haspart", "title": { "de": "Umfasst folgenden Teil", "en": "Has part" } }, "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "scheme": "url" } ], "resource_type": { "id": "publication-workingpaper", "title": { "de": "Arbeitspapier", "en": "Working paper" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Deep Learning" }, { "subject": "social sciences" }, { "subject": "humanities" }, { "subject": "ISIDORE" }, { "subject": "Keras" }, { "subject": "text classifier" } ], "title": "A classifier using ISIDORE, the social and humanities search engine and Keras API for Deep Learning", "version": "V1" }, "parent": { "access": { "owned_by": { "user": 116802 } }, "communities": { "default": "8d543e2f-8ce4-4505-ab1f-219d45b70661", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2020-08-17T08:10:38.943685+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "8d543e2f-8ce4-4505-ab1f-219d45b70661", "links": {}, "metadata": { "curation_policy": "We accept all papers, notebooks, etc. from HN Lab and Huma-Num members.
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\r\n\r\nSupported by the CNRS (the French National Center for Scientific Research), Aix-Marseille University and the Campus Condorcet, Huma-Num is a French Very Large Research Infrastructure (“Très Grande Infrastructure de Recherche”, TGIR) with international reach devoted to Social Sciences and Humanities. It is part of the national ESFRI roadmap, which is in turn aligned with the European Union’s ESFRI framework. Indeed, Huma-Num is entrusted with France’s participation in two European Research Infrastructure Consortia (ERIC): DARIAH (Digital Research Infrastructure for the Arts and Humanities) and CLARIN (Common Language Resources and Technologies Infrastructure). It is also involved in European and international projects.
\r\n\r\nHuma-Num aims at supporting research communities by providing services, assessment and tools on digital research data. To perform its missions, the TGIR Huma-Num bases its activities on a innovative form of organization that combines human (collective consultation through Huma-Num’s consortia, which are groups of researchers and engineers, funded by Huma-Num, working on common areas of interest) and technological resources (sustainable digital services) on a national and European scale.
\r\n\r\nWith the consortia it supervises, Huma-Num coordinates the production of digital data while offering a variety of platforms and tools for the processing, conservation, dissemination and long-term preservation of digital research data. One of the scientific objective of such involvement is to promote data sharing so that other researchers, communities or disciplines, can reuse them, including from an interdisciplinary perspective and in different ways. More generally, the principles and methods of the Web of data (RDF, SPARQL, SKOS, OWL) on which Huma-Num’s services rely enable data to be documented or re-documented for various uses without confining them to inaccessible silos.
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