Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Semantic resources"'
Autor:
Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David, Leyla Garcia, Brandon Whitehead, Catherine Roussey, Armando Stellato, Ferdinando Villa
Publikováno v:
Data Science Journal, Vol 22, Iss 1 (2023)
This article details a correction to the article: Caracciolo, C., Aubin, S., Jonquet, C., Amdouni, E., David, R., Garcia, L., Whitehead, B., Roussey, C., Stellato, A. and Villa, F., 2020. 39 Hints to Facilitate the Use of Semantics for Data on Agricu
Externí odkaz:
https://doaj.org/article/e1be0d4ca2c142f4b2883fd08cdcdab0
Publikováno v:
Data in Brief, Vol 45, Iss , Pp 108680- (2022)
The main objective of the project LEAP4FNSSA (Long-term EU-AU Research and Innovation Partnership for Food and Nutrition Security and Sustainable Agriculture) is to provide a tool for European and African institutions to engage in a sustainable partn
Externí odkaz:
https://doaj.org/article/e449fa37b9e844a5b7b7ac366cd819cd
Publikováno v:
Towards a Semantic Network Enriched with a Variety of Semantic Relations. :87-120
Externí odkaz:
https://www.ceeol.com/search/chapter-detail?id=889551
Akademický článek
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Publikováno v:
International Journal of the Commons, Vol 16, Iss 1 (2022)
Commons traditionally refer to shared natural resources that are at risk of being depleted or even destroyed. The rules established by commoners offer a way to manage such scarce resources. Through a series of projects on national eHealth patient hea
Externí odkaz:
https://doaj.org/article/bdb6db57fc8f441f9ce6d3a870a163c6
Autor:
Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David, Leyla Garcia, Brandon Whitehead, Catherine Roussey, Armando Stellato, Ferdinando Villa
Publikováno v:
Data Science Journal, Vol 19, Iss 1 (2020)
In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of dat
Externí odkaz:
https://doaj.org/article/8f6c2f1e04c4458b929a4e350c6eb445
Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da67d6a1b71dcf96a97b63ee6284d9d5
https://zenodo.org/record/7322054
https://zenodo.org/record/7322054
Akademický článek
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Publikováno v:
Journées LIFT 2021-Linguistique informatique, formelle et de terrain
Journées LIFT 2021-Linguistique informatique, formelle et de terrain, Dec 2021, Grenoble, France
Journées LIFT 2021-Linguistique informatique, formelle et de terrain, Dec 2021, Grenoble, France
In this paper, we report our efforts to convert one of the most comprehensive lexicographic resources of French, the Trésor de la Langue Française, into the Ontolex-Lemon model. Despite the widespread usage of this resource, the original XML format
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e8b9d993286551f18e8136815995ad9
https://hal.inria.fr/hal-03463294/document
https://hal.inria.fr/hal-03463294/document