Littérature scientifique sur le sujet « Modèles sémantiques vectoriels »
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Articles de revues sur le sujet "Modèles sémantiques vectoriels"
Ataa Allah, Fadoua, Siham Boulaknadel, Abderrahim El Qadi et Driss Aboutajdine. « Evaluation de l'analyse sémantique latente et du modèle vectoriel standard appliqués à la langue arabe ». Techniques et sciences informatiques 27, no 7 (30 septembre 2008) : 851–77. http://dx.doi.org/10.3166/tsi.27.851-877.
Texte intégralThèses sur le sujet "Modèles sémantiques vectoriels"
Allanic, Hervé. « Utilisation du modèle vectoriel topologique pour la reconnaissance des formes ». Paris 12, 2002. http://www.theses.fr/2002PA120026.
Texte intégralThis thesis presents a work on an image understanding system based on semantic networks which aim is to realize data fusion with symbolic primitives extracted from an image. We use a Topological Vectorial Model (MVT) which is broadly used in the Geographic Information Systems. This model can handle semantic informations together with geometrical datas. After looking at the relevance of the MVT for the image understanding area, we have defined the characteristics of a system based on this model. We have then been able to define data and task types, to create system architecture and to validate the approach on two applications
Ventresque, Anthony. « Espaces vectoriels sémantiques : enrichissement et interprétation de requêtes dans un système d'information distribué et hétérogène ». Phd thesis, Université de Nantes, 2008. http://tel.archives-ouvertes.fr/tel-00457820.
Texte intégralMiletic, Filip. « An investigation into contact-induced semantic shifts in Quebec English : conciliating corpus-based vector models and variationist sociolinguistic inquiry ». Electronic Thesis or Diss., Toulouse 2, 2022. http://www.theses.fr/2022TOU20034.
Texte intégralThis dissertation investigates contact-induced semantic shifts in Quebec English, i.e., preexisting English words which are used with a different meaning due to the potential influence of French. I propose a novel approach at the intersection of natural language processing and variationist sociolinguistics, aiming to provide a more comprehensive descriptive account as well as assess the contributions of the implemented methods.In order to conduct computational analyses of semantic variation, I created a corpus containing 78.8 million tweets from Montreal, Toronto, and Vancouver. It was used to implement different types of vector space models, i.e., computational representations of word meaning. Type-level models were used to identify new semantic shifts based on the semantic differences between Montreal and the other two cities. Token-level models were used in finer-grained analyses and allowed to further characterize their use. Despite promising results, systematic quantitative evaluation and extensive qualitative analyses suggest that these methods are hampered by noise related to their inherent characteristics as well as corpus structure.These large-scale approaches were complemented with finer-grained data collected through sociolinguistic interviews with 15 speakers living in Montreal. Varying correlations between lexical items and a range of sociodemographic factors, coupled with qualitative remarks on their use, point to four distinct patterns of synchronic variation; these in turn reflect potential diachronic processes. Interspeaker variability suggests that the use of semantic shifts is driven by speakers who tend to be younger and proficient in both English and French. The acceptability ratings are weakly correlated with computational variation measures, suggesting that they capture different dimensions of semantic variation.Overall, this dissertation has provided the first systematic description of contact-induced semantic shifts in Quebec English, and highlighted the complementarity of approaches used in different disciplines. These considerations have provided a pathway towards a better-informed use of corpus-based computational methods in studies of sociolinguistic phenomena
Périnet, Amandine. « Analyse distributionnelle appliquée aux textes de spécialité : réduction de la dispersion des données par abstraction des contextes ». Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCD056/document.
Texte intégralIn specialised domains, the applications such as information retrieval for machine translation rely on terminological resources for taking into account terms or semantic relations between terms or groupings of terms. In order to face up to the cost of building these resources, automatic methods have been proposed. Among those methods, the distributional analysis uses the repeated information in the contexts of the terms to detect a relation between these terms. While this hypothesis is usually implemented with vector space models, those models suffer from a high number of dimensions and data sparsity in the matrix of contexts. In specialised corpora, this contextual information is even sparser and less frequent because of the smaller size of the corpora. Likewise, complex terms are usually ignored because of their very low number of occurrences. In this thesis, we tackle the problem of data sparsity on specialised texts. We propose a method that allows making the context matrix denser, by performing an abstraction of distributional contexts. Semantic relations acquired from corpora are used to generalise and normalise those contexts. We evaluated the method robustness on four corpora of different sizes, different languages and different domains. The analysis of the results shows that, while taking into account complex terms in distributional analysis, the abstraction of distributional contexts leads to defining semantic clusters of better quality, that are also more consistent and more homogeneous