Literatura científica selecionada sobre o tema "Représentation sémantique profonde"
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Artigos de revistas sobre o assunto "Représentation sémantique profonde"
Plotnikov, Nikolaï. "La ‘personne’ et le ‘sujet’. La sémantique de la personnalité/personaltät dans l’histoire intellectuelle russe". Cahiers du Centre de Linguistique et des Sciences du Langage, n.º 29 (26 de fevereiro de 2011): 231–49. http://dx.doi.org/10.26034/la.cdclsl.2011.935.
Texto completo da fonteNagacevschi Josan, Erica. "L’expression des émotions dans “Frappe-toi le Coeur” d’Amélie Nothomb". Acta Universitatis Lodziensis. Folia Litteraria Romanica 18, n.º 1 (30 de outubro de 2023): 99–110. http://dx.doi.org/10.18778/1505-9065.18.09.
Texto completo da fonteBERTRAND, Denis. "La générativité est-elle soluble dans le sensible ? Réflexions topologiques et énonciatives « au cœur » du parcours génératif". 130, n.º 130 (23 de janeiro de 2024). http://dx.doi.org/10.25965/as.8295.
Texto completo da fonteHébert, Martin. "Paix". Anthropen, 2018. http://dx.doi.org/10.17184/eac.anthropen.088.
Texto completo da fonteTeses / dissertações sobre o assunto "Représentation sémantique profonde"
Hijazi, Rita. "Simplification syntaxique de textes à base de représentations sémantiques exprimées avec le formalisme Dependency Minimal Recursion Semantics (DMRS)". Electronic Thesis or Diss., Aix-Marseille, 2022. http://theses.univ-amu.fr.lama.univ-amu.fr/221214_HIJAZI_602vzfxdu139bxtesm225byk629aeqyvw_TH.pdf.
Texto completo da fonteText simplification is the task of making a text easier to read and understand and more accessible to a target audience. This goal can be reached by reducing the linguistic complexity of the text while preserving the original meaning as much as possible. This thesis focuses on the syntactic simplification of texts in English, a task for which these automatic systems have certain limitations. To overcome them, we first propose a new method of syntactic simplification exploiting semantic dependencies expressed in DMRS (Dependency Minimal Recursion Semantics), a deep semantic representation in the form of graphs combining semantics and syntax. Syntactic simplification enables to represent the complex sentence in a DMRS graph, transforming this graph according to specific strategies into other DMRS graphs, which will generate simpler sentences. This method allows the syntactic simplification of complex constructions, in particular division operations such as subordinate clauses, appositive clauses, coordination and also the transformation of passive forms into active forms. The results obtained by this system of syntactic simplification surpass those of the existing systems of the same type in the production of simple, grammatical sentences and preserving the meaning, thus demonstrating all the interest of our approach to syntactic simplification based on semantic representations in DMRS
Grandemange, Philippe. "Représentation des connaissances et profondeur variable : une implantation". Paris 13, 1992. http://www.theses.fr/1992PA132008.
Texto completo da fonteFrancis, Danny. "Représentations sémantiques d'images et de vidéos". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS605.
Texto completo da fonteRecent research in Deep Learning has sent the quality of results in multimedia tasks rocketing: thanks to new big datasets of annotated images and videos, Deep Neural Networks (DNN) have outperformed other models in most cases. In this thesis, we aim at developing DNN models for automatically deriving semantic representations of images and videos. In particular we focus on two main tasks : vision-text matching and image/video automatic captioning. Addressing the matching task can be done by comparing visual objects and texts in a visual space, a textual space or a multimodal space. Based on recent works on capsule networks, we define two novel models to address the vision-text matching problem: Recurrent Capsule Networks and Gated Recurrent Capsules. In image and video captioning, we have to tackle a challenging task where a visual object has to be analyzed, and translated into a textual description in natural language. For that purpose, we propose two novel curriculum learning methods. Moreover regarding video captioning, analyzing videos requires not only to parse still images, but also to draw correspondences through time. We propose a novel Learned Spatio-Temporal Adaptive Pooling method for video captioning that combines spatial and temporal analysis. Extensive experiments on standard datasets assess the interest of our models and methods with respect to existing works
Harrando, Ismail. "Representation, information extraction, and summarization for automatic multimedia understanding". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS097.
Texto completo da fonteWhether on TV or on the internet, video content production is seeing an unprecedented rise. Not only is video the dominant medium for entertainment purposes, but it is also reckoned to be the future of education, information and leisure. Nevertheless, the traditional paradigm for multimedia management proves to be incapable of keeping pace with the scale brought about by the sheer volume of content created every day across the disparate distribution channels. Thus, routine tasks like archiving, editing, content organization and retrieval by multimedia creators become prohibitively costly. On the user side, too, the amount of multimedia content pumped daily can be simply overwhelming; the need for shorter and more personalized content has never been more pronounced. To advance the state of the art on both fronts, a certain level of multimedia understanding has to be achieved by our computers. In this research thesis, we aim to go about the multiple challenges facing automatic media content processing and analysis, mainly gearing our exploration to three axes: 1. Representing multimedia: With all its richness and variety, modeling and representing multimedia content can be a challenge in itself. 2. Describing multimedia: The textual component of multimedia can be capitalized on to generate high-level descriptors, or annotations, for the content at hand. 3. Summarizing multimedia: we investigate the possibility of extracting highlights from media content, both for narrative-focused summarization and for maximising memorability