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Ce document présente la création d’un classifieur de texte permettant de prédire l’ap- partenance disciplinaire de titres d’ouvrages ou d’articles en sciences humaines et sociales. Il met en œuvre ISIDORE et l’API Keras pour l’apprentissage profond (Deep Learning).
Jupyter notebook is available at https://gitlab.huma-num.fr/spouyllau/isidore-jupyter. An english version is available at https://doi.org/10.5281/zenodo.3994126
Apprentissage profond, Deep Learning, ISIDORE, histoire, https://data.bnf.fr/ark:/12148/cb17706295v, sciences humaines et sociales, http://id.loc.gov/authorities/subjects/sh85079324, Classifieur de text
Apprentissage profond, Deep Learning, ISIDORE, histoire, https://data.bnf.fr/ark:/12148/cb17706295v, sciences humaines et sociales, http://id.loc.gov/authorities/subjects/sh85079324, Classifieur de text
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |