Zobrazeno 1 - 10 of 19 pro vyhledávání: '"Arnaud Ferré"'
1
Akademický článek
An analysis of entity normalization evaluation biases in specialized domains
Autor: Arnaud Ferré, Philippe Langlais
Publikováno v: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-29 (2023)
Abstract Background Entity normalization is an important information extraction task which has recently gained attention, particularly in the clinical/biomedical and life science domains. On several datasets, state-of-the-art methods perform rather w
Externí odkaz: https://doaj.org/article/ea738e3048574b77863ec901a933ab6d
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2
Akademický článek
C-Norm: a neural approach to few-shot entity normalization
Autor: Arnaud Ferré, Louise Deléger, Robert Bossy, Pierre Zweigenbaum, Claire Nédellec
Publikováno v: BMC Bioinformatics, Vol 21, Iss S23, Pp 1-19 (2020)
Abstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specializ
Externí odkaz: https://doaj.org/article/88f113a8208d4915abbd31ee9b1d0d68
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3
Akademický článek
The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes
Autor: Vincent J. Henry, Anne Goelzer, Arnaud Ferré, Stephan Fischer, Marc Dinh, Valentin Loux, Christine Froidevaux, Vincent Fromion
Publikováno v: Journal of Biomedical Semantics, Vol 8, Iss 1, Pp 1-16 (2017)
Abstract Background High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and
Externí odkaz: https://doaj.org/article/05ad1b166fa04ac7a06f09131ab91a7c
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4
Akademický článek
Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data
Autor: Arnaud Ferré, Mouhamadou Ba, Robert Bossy
Publikováno v: Genomics & Informatics, Vol 17, Iss 2 (2019)
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimal
Externí odkaz: https://doaj.org/article/2dcffa9df61f4c95b39887f3a76ee59d
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6
Dégradation d’un anticancéreux dans les eaux usées
Autor: Mahnaz Sabeti-Azad, Arthur Radoux, Raphaël Guegan, William Briand, Sylvie Lautru, Britany Marta, Clémence Maupu, Julie Rojahn, Arnaud Ferré, Stéphanie Bury-Moné, Olivier Namy, Céline Aubry, Ousmane Dao, Guillaume Garnier, Yueying Zhu, Phillipe Bouloc, Kenn Papadopoulo, Julie Miesch
Publikováno v: médecine/sciences
médecine/sciences, 2018, 34 (12), pp.1111--1114. ⟨10.1051/medsci/2018304⟩
médecine/sciences, EDP Sciences, 2018, 34 (12), pp.1111--1114. ⟨10.1051/medsci/2018304⟩
iGEM (pour international genetically engineered machine) est un concours international autour de la biologie synthétique réunissant des étudiants de toutes disciplines (mathématiques, physique, biologie, arts, etc.). « L’objectif est de constr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ff7c6129b3b2d66a5c2bf8accf08fc1
https://doi.org/10.1051/medsci/2018304
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7
Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information
Autor: Arnaud Ferré, Robert Bossy, Mouhamadou Ba, Louise Deleger, Thomas Lavergne, Pierre Zweigenbaum, Claire Nédellec
Publikováno v: 12th Conference on Language Resources and Evaluation
12th Conference on Language Resources and Evaluation, May 2020, Marseille, France. pp.1959-1966
HAL
International audience; Entity normalization (or entity linking) is an important subtask of information extraction that links entity mentions in text to categories or concepts in a reference vocabulary. Machine learning based normalization methods ha
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::063fec80e9d61c940b28a41e3405cfc7
https://hal.inrae.fr/hal-02866789/file/2020.lrec-1.241.pdf
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8
Improving methods for normalizing biomedical text entities with concepts from an ontology with (almost) no training data at BLAH5 the CONTES
Autor: Robert Bossy, Arnaud Ferré, Mouhamadou Ba
Publikováno v: Genomics & Informatics
Genomics & Informatics, 2019, 17 (2), pp.e20. ⟨10.5808/GI.2019.17.2.e20⟩
International audience; Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An o
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09eb85255fd3d3c00157eb118e767bc0
https://hal.inrae.fr/hal-02947689
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9
Combining rule-based and embedding-based approaches to normalize textual entities with an ontology
Autor: Arnaud Ferré, Louise Deléger, Pierre Zweigenbaum, Claire Nédellec
Publikováno v: International Conference on Language Resources and Evaluation
International Conference on Language Resources and Evaluation, May 2018, Miyazaki, Japan
Claire Nédellec
Scopus-Elsevier
HAL
International audience; In this paper, we propose a two-step method to normalize multi-word terms with concepts from a domain-specific ontology. Normalization is a critical step of information extraction. The method uses vector representations of ter
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::68363756431a1e051860a5bd340f183f
https://hal.archives-ouvertes.fr/hal-01899826
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10
Meet-U: educating through research immersion
Autor: Anne Lopes, Laurent David, Sophie Sacquin-Mora, Alexandre Albani, Marc Delarue, Vasiliki Myrodia, Eric Anthony, Mélissa Cardon, Hugo Schweke, Nicolas Sompairac, Stéphane Le Crom, Christine Froidevaux, Patrick F.J. Fuchs, Pierre Poulain, Violaine Verrecchia, Jeanne Laurentie, Nadège Guiglielmoni, Raphael Guerois, Martin Weigt, Maria Ruiz-Cuevas, Juliana S Bernardes, Samer Abboud, Rémi Montagne, Nathalie Lehmann, Vaitea Opuu, Chloé Quignot, Agnes Baud, Camille Lochard, Chloé Dequeker, Samia Djerroud, Romain Conte, Elodie Laine, Dariusz Czernecki, Olivier Lespinet, Mariam Sissoko, Agathe Delaune, Guillaume Bouvier, Arnaud Ferré, Audrey Vallerix, Robert Clerc, Hugues Richard, Alessandra Carbone, Elise Parey, Sylvain Privé, Pauline Fourgoux, Nika Abdollahi, Yann Ponty, Lélia Polit, Gaëlle Lelandais
Publikováno v: PLoS Computational Biology
PLoS Computational Biology, Public Library of Science, 2018, 14 (3), pp.1-10. ⟨10.1371/journal.pcbi.1005992⟩
PLoS Computational Biology, 2018, 14 (3), pp.e1005992. ⟨10.1371/journal.pcbi.1005992⟩
PLoS Computational Biology, 2018, 14 (3), pp.1-10. ⟨10.1371/journal.pcbi.1005992⟩
PLoS Computational Biology, Public Library of Science, 2018, pp.1-10. 〈10.1371/journal.pcbi.1005992〉
Plos Computational Biology 3 (14), 1-10. (2018)
PLoS Computational Biology, Vol 14, Iss 3, p e1005992 (2018)
This is a PLOS Computational Biology Education paper.; International audience; We present a new educational initiative, called Meet-U, that aims at training students to collaborativework in computational biology and at bridging the gap between educat
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16f467a14d11c3e741a5d2eeed3fac59
https://hal.inria.fr/hal-01722019/document
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