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  • Open Access English
    Authors: 
    Maud Ehrmann; Matteo Romanello; Antoine Doucet; Simon Clematide;
    Publisher: HAL CCSD
    Country: Switzerland
    Project: EC | NewsEye (770299)

    We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.

  • Open Access
    Authors: 
    Lucie Martin; Claire Delhon; Alexa Dufraisse; Stéphanie Thiébault; Marie Besse;
    Publisher: Éditions du Comité des travaux historiques et scientifiques
    Countries: Switzerland, France

    Au Néolithique, les montagnes sont exploitées pour leurs ressources minérales, cynégétiques et pastorales. À partir de 5 500 ans avant notre ère, les premières communautés agropastorales atteignent les Alpes depuis le nord de l’Italie et la vallée du Rhône et s’établissent dans les massifs subalpins comme dans les Alpes internes. Les études archéobotaniques (analyse des macrorestes végétaux, principalement des graines, des fruits et des charbons de bois) permettent de comprendre l’économie végétale de ces communautés néolithiques : quelles espèces, sauvages ou cultivées, étaient récoltées pour le fourrage, pour construire, se nourrir, se soigner, se chauffer ? Les données de cinq sites néolithiques nous indiquent les différentes façons dont ces populations ont exploité leur territoire en tirant profit des ressources de divers biotopes, de l’étage collinéen à l’étage alpin, contribuant ainsi à mieux comprendre la mobilité verticale au Néolithique en contexte alpin. During the Neolithic, mountains were exploited for their mineral, hunting and pastoral resources. The first agro-pastoral communities reached the Alps from Northern Italy and the Rhone valley and settled in the subalpine massifs and in the internal Alps. Archeobotanical studies (plant macroremains and charcoal analysis) conducted at five sites allow us to understand the plant economy of these Neolithic communities: they determine which crops were cultivated, used as fodder, or gathered for consumption, medicine or other purpose, such as firewood. In the present paper, we support that the use of plant resources and the exploitation of territory are very different for the same period from one region to another, depending on the activities carried out at each site but also on cultural backgrounds. Archeobotanical data indicate how these people took resources from various plant associations growing from the colline to the subalpine level, and thus contribute to the understanding of vertical mobility in alpine contexts.

  • Publication . Part of book or chapter of book . 2019
    Open Access
    Authors: 
    Elisa Nury;
    Country: Switzerland

    International audience; This paper describes the workflow of the Grammateus project, from gathering data on Greek documentary papyri to the creation of a web application. The first stage is the selection of a corpus and the choice of metadata to record: papyrology specialists gather data from printed editions, existing online resources and digital facsimiles. In the next step, this data is transformed into the EpiDoc standard of XML TEI encoding, to facilitate its reuse by others, and processed for HTML display. We also reuse existing text transcriptions available on . Since these transcriptions may be regularly updated by the scholarly community, we aim to access them dynamically. Although the transcriptions follow the EpiDoc guidelines, the wide diversity of the papyri as well as small inconsistencies in encoding make data reuse challenging. Currently, our data is available on an institutional GitLab repository, and we will archive our final dataset according to the FAIR principles.

  • Open Access English
    Authors: 
    Anne Mayor; Douze Katja; Maria Lorenzo Martinez; Miriam Truffa Giachet; Aymeric Jacques; Hamady Bocoum; Champion Louis; Cervera Camille; Sarah Davidoux; Aline Garnier; +13 more
    Publisher: HAL CCSD
    Countries: France, Switzerland

    Cet article présente les résultats de la campagne de terrain menée au Sénégal oriental en 2017 dans le cadre du programme international « Peuplement humain et paléoenvironnement en Afrique ». Il intègre les résultats de deux projets complémentaires : le projet ANR-FNS CheRCHA, ainsi que le projet FNS Falémé. Le premier vise à reconstituer le cadre chronostratigraphique et les évolutions culturelles au Pléistocène et à l'Holocène ancien et moyen dans la vallée de la Falémé, tandis que le second est ciblé sur les dynamiques techniques des deux derniers millénaires au Sénégal oriental.

  • Publication . Preprint . Other literature type . Part of book or chapter of book . Article . Conference object . 2020
    Open Access English
    Authors: 
    Diego Marcos; Ruth Fong; Sylvain Lobry; Rémi Flamary; Nicolas Courty; Devis Tuia;
    Publisher: HAL CCSD
    Countries: Switzerland, Netherlands, France, France, France
    Project: ANR | 3IA@cote d'azur (ANR-19-P3IA-0002), ANR | OATMIL (ANR-17-CE23-0012)

    International audience; Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpretable attributes that are present in the scene. We call such an intermediate layer a \emph{semantic bottleneck}. Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision. In this paper, we look into semantic bottlenecks that capture context: we want attributes to be in groups of a few meaningful elements and participate jointly to the final decision. We use a two-layer semantic bottleneck that gathers attributes into interpretable, sparse groups, allowing them contribute differently to the final output depending on the context. We test our contextual semantic interpretable bottleneck (CSIB) on the task of landscape scenicness estimation and train the semantic interpretable bottleneck using an auxiliary database (SUN Attributes). Our model yields in predictions as accurate as a non-interpretable baseline when applied to a real-world test set of Flickr images, all while providing clear and interpretable explanations for each prediction.

  • Open Access
    Authors: 
    Yang, Xiucheng; Koehl, Mathieu; Grussenmeyer, Pierre;
    Publisher: MDPI
    Country: France
search
Include:
6 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Maud Ehrmann; Matteo Romanello; Antoine Doucet; Simon Clematide;
    Publisher: HAL CCSD
    Country: Switzerland
    Project: EC | NewsEye (770299)

    We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.

  • Open Access
    Authors: 
    Lucie Martin; Claire Delhon; Alexa Dufraisse; Stéphanie Thiébault; Marie Besse;
    Publisher: Éditions du Comité des travaux historiques et scientifiques
    Countries: Switzerland, France

    Au Néolithique, les montagnes sont exploitées pour leurs ressources minérales, cynégétiques et pastorales. À partir de 5 500 ans avant notre ère, les premières communautés agropastorales atteignent les Alpes depuis le nord de l’Italie et la vallée du Rhône et s’établissent dans les massifs subalpins comme dans les Alpes internes. Les études archéobotaniques (analyse des macrorestes végétaux, principalement des graines, des fruits et des charbons de bois) permettent de comprendre l’économie végétale de ces communautés néolithiques : quelles espèces, sauvages ou cultivées, étaient récoltées pour le fourrage, pour construire, se nourrir, se soigner, se chauffer ? Les données de cinq sites néolithiques nous indiquent les différentes façons dont ces populations ont exploité leur territoire en tirant profit des ressources de divers biotopes, de l’étage collinéen à l’étage alpin, contribuant ainsi à mieux comprendre la mobilité verticale au Néolithique en contexte alpin. During the Neolithic, mountains were exploited for their mineral, hunting and pastoral resources. The first agro-pastoral communities reached the Alps from Northern Italy and the Rhone valley and settled in the subalpine massifs and in the internal Alps. Archeobotanical studies (plant macroremains and charcoal analysis) conducted at five sites allow us to understand the plant economy of these Neolithic communities: they determine which crops were cultivated, used as fodder, or gathered for consumption, medicine or other purpose, such as firewood. In the present paper, we support that the use of plant resources and the exploitation of territory are very different for the same period from one region to another, depending on the activities carried out at each site but also on cultural backgrounds. Archeobotanical data indicate how these people took resources from various plant associations growing from the colline to the subalpine level, and thus contribute to the understanding of vertical mobility in alpine contexts.

  • Publication . Part of book or chapter of book . 2019
    Open Access
    Authors: 
    Elisa Nury;
    Country: Switzerland

    International audience; This paper describes the workflow of the Grammateus project, from gathering data on Greek documentary papyri to the creation of a web application. The first stage is the selection of a corpus and the choice of metadata to record: papyrology specialists gather data from printed editions, existing online resources and digital facsimiles. In the next step, this data is transformed into the EpiDoc standard of XML TEI encoding, to facilitate its reuse by others, and processed for HTML display. We also reuse existing text transcriptions available on . Since these transcriptions may be regularly updated by the scholarly community, we aim to access them dynamically. Although the transcriptions follow the EpiDoc guidelines, the wide diversity of the papyri as well as small inconsistencies in encoding make data reuse challenging. Currently, our data is available on an institutional GitLab repository, and we will archive our final dataset according to the FAIR principles.

  • Open Access English
    Authors: 
    Anne Mayor; Douze Katja; Maria Lorenzo Martinez; Miriam Truffa Giachet; Aymeric Jacques; Hamady Bocoum; Champion Louis; Cervera Camille; Sarah Davidoux; Aline Garnier; +13 more
    Publisher: HAL CCSD
    Countries: France, Switzerland

    Cet article présente les résultats de la campagne de terrain menée au Sénégal oriental en 2017 dans le cadre du programme international « Peuplement humain et paléoenvironnement en Afrique ». Il intègre les résultats de deux projets complémentaires : le projet ANR-FNS CheRCHA, ainsi que le projet FNS Falémé. Le premier vise à reconstituer le cadre chronostratigraphique et les évolutions culturelles au Pléistocène et à l'Holocène ancien et moyen dans la vallée de la Falémé, tandis que le second est ciblé sur les dynamiques techniques des deux derniers millénaires au Sénégal oriental.

  • Publication . Preprint . Other literature type . Part of book or chapter of book . Article . Conference object . 2020
    Open Access English
    Authors: 
    Diego Marcos; Ruth Fong; Sylvain Lobry; Rémi Flamary; Nicolas Courty; Devis Tuia;
    Publisher: HAL CCSD
    Countries: Switzerland, Netherlands, France, France, France
    Project: ANR | 3IA@cote d'azur (ANR-19-P3IA-0002), ANR | OATMIL (ANR-17-CE23-0012)

    International audience; Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpretable attributes that are present in the scene. We call such an intermediate layer a \emph{semantic bottleneck}. Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision. In this paper, we look into semantic bottlenecks that capture context: we want attributes to be in groups of a few meaningful elements and participate jointly to the final decision. We use a two-layer semantic bottleneck that gathers attributes into interpretable, sparse groups, allowing them contribute differently to the final output depending on the context. We test our contextual semantic interpretable bottleneck (CSIB) on the task of landscape scenicness estimation and train the semantic interpretable bottleneck using an auxiliary database (SUN Attributes). Our model yields in predictions as accurate as a non-interpretable baseline when applied to a real-world test set of Flickr images, all while providing clear and interpretable explanations for each prediction.

  • Open Access
    Authors: 
    Yang, Xiucheng; Koehl, Mathieu; Grussenmeyer, Pierre;
    Publisher: MDPI
    Country: France
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