Literatura académica sobre el tema "Classification texte"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Classification texte".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Classification texte"
Garrido, Carlos. "Deficiencias del texto de partida en la traducción de textos destinados a la enseñanza y divulgación de la ciencia". Meta 60, n.º 3 (5 de abril de 2016): 454–75. http://dx.doi.org/10.7202/1036138ar.
Texto completoHoek, Leo H. "Timbres-poste et intermédialité". Protée 30, n.º 2 (9 de julio de 2003): 33–44. http://dx.doi.org/10.7202/006729ar.
Texto completoHurley, Robert. "Le genre « évangile » en fonction des effets produits par la mise en intrigue de Jésus". Laval théologique et philosophique 58, n.º 2 (27 de noviembre de 2002): 243–57. http://dx.doi.org/10.7202/000359ar.
Texto completoGARON-AUDY, Muriel. "La logique de l’acte de classification : postulat ou question pour l’analyse de la mobilité". Sociologie et sociétés 8, n.º 2 (30 de septiembre de 2002): 37–60. http://dx.doi.org/10.7202/001283ar.
Texto completoCazé, Antoine. "Poésie, texte métissé, une lecture de David Antin". Recherches anglaises et nord-américaines 21, n.º 1 (1988): 21–28. http://dx.doi.org/10.3406/ranam.1988.1187.
Texto completoBonafin, Massimo. "Fra filologia e antropologiala genesi del lupo e della volpe". Reinardus / Yearbook of the International Reynard Society 11 (15 de noviembre de 1998): 25–35. http://dx.doi.org/10.1075/rein.11.03bon.
Texto completoZakharia, Katia. "Les Mille et une nuits. Histoire du texte et classification des contes". Arabica 56, n.º 1 (2009): 132–34. http://dx.doi.org/10.1163/157005809x398708.
Texto completoPiscini, Gianluca. "Sources, cibles et structure de deux réflexions de Porphyre sur l’athéisme (commentaire sur le Timée, fragment 28 Sodano ; Lettre à Marcella 21-23)". Revue des Études Grecques 134, n.º 1 (2021): 143–75. http://dx.doi.org/10.3406/reg.2021.8674.
Texto completoGagnon, Mathieu. "Penser la question des rapports aux savoirs en éducation : clarification et besoin de recherches conceptuelles". Les ateliers de l'éthique 6, n.º 1 (28 de marzo de 2018): 30–42. http://dx.doi.org/10.7202/1044300ar.
Texto completoCaparros, Ernest. "La nature juridique commune du patrimoine familial et de la société d’acquêts". Revue générale de droit 30, n.º 1 (1 de diciembre de 2014): 1–60. http://dx.doi.org/10.7202/1027599ar.
Texto completoTesis sobre el tema "Classification texte"
Tisserant, Guillaume. "Généralisation de données textuelles adaptée à la classification automatique". Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS231/document.
Texto completoWe have work for a long time on the classification of text. Early on, many documents of different types were grouped in order to centralize knowledge. Classification and indexing systems were then created. They make it easy to find documents based on readers' needs. With the increasing number of documents and the appearance of computers and the internet, the implementation of text classification systems becomes a critical issue. However, textual data, complex and rich nature, are difficult to treat automatically. In this context, this thesis proposes an original methodology to organize and facilitate the access to textual information. Our automatic classification approache and our semantic information extraction enable us to find quickly a relevant information.Specifically, this manuscript presents new forms of text representation facilitating their processing for automatic classification. A partial generalization of textual data (GenDesc approach) based on statistical and morphosyntactic criteria is proposed. Moreover, this thesis focuses on the phrases construction and on the use of semantic information to improve the representation of documents. We will demonstrate through numerous experiments the relevance and genericity of our proposals improved they improve classification results.Finally, as social networks are in strong development, a method of automatic generation of semantic Hashtags is proposed. Our approach is based on statistical measures, semantic resources and the use of syntactic information. The generated Hashtags can then be exploited for information retrieval tasks from large volumes of data
Danuser, Hermann. "Der Text und die Texte. Über Singularisierung und Pluralisierung einer Kategorie". Bärenreiter Verlag, 1998. https://slub.qucosa.de/id/qucosa%3A36795.
Texto completoVasil'eva, Natalija. "Eigennamen in der Welt zeitgenössischer Texte". Gesellschaft für Namenkunde e.V, 2007. https://ul.qucosa.de/id/qucosa%3A31517.
Texto completoLasch, Alexander. "Texte im Handlungsbereich der Religion". De Gruyter, 2011. https://tud.qucosa.de/id/qucosa%3A74840.
Texto completoSayadi, Karim. "Classification du texte numérique et numérisé. Approche fondée sur les algorithmes d'apprentissage automatique". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066079/document.
Texto completoDifferent disciplines in the humanities, such as philology or palaeography, face complex and time-consuming tasks whenever it comes to examining the data sources. The introduction of computational approaches in humanities makes it possible to address issues such as semantic analysis and systematic archiving. The conceptual models developed are based on algorithms that are later hard coded in order to automate these tedious tasks. In the first part of the thesis we propose a novel method to build a semantic space based on topics modeling. In the second part and in order to classify historical documents according to their script. We propose a novel representation learning method based on stacking convolutional auto-encoder. The goal is to automatically learn plot representations of the script or the written language
Schneider, Ulrich Johannes. "Über Tempel und Texte: ein Bildervergleich". Fink, 1999. https://ul.qucosa.de/id/qucosa%3A12768.
Texto completoAKAMA, HIROYUKI. "Tableau, corps, texte : etudes historiques sur la classification-recit en france au xixe siecle". Paris 1, 1992. http://www.theses.fr/1992PA010604.
Texto completoThrough the "story" named ideology (of cabanis and tracy), the history of nineteenth-century "classification" can be divided into three distinctive stages having their own means to embody the complex of "table-body-text" (tableau-corps-texte). Primarily, an epistemological rupture of the "table" (tableau) which was a matter for the element of ideology, and in consequence, the phenomena of "inner dia-textuality" suppressing the possibilities of this current of thought. Secondly, the appearance of some cone-shaped encyclopedic spaces of knowledges, and their transformations to materialize the "positivist" significations of sociology and anthropology. Thirdly, another rupture of the "table" (tableau), as a result of which the spaces of knowledges became fluid to be symbols of anti-positivist crisis of science, and finally a tripartite unity of "discources" (discours) emerged : hysteria, science-fiction and institute-university
Felhi, Mehdi. "Document image segmentation : content categorization". Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109/document.
Texto completoIn this thesis I discuss the document image segmentation problem and I describe our new approaches for detecting and classifying document contents. First, I discuss our skew angle estimation approach. The aim of this approach is to develop an automatic approach able to estimate, with precision, the skew angle of text in document images. Our method is based on Maximum Gradient Difference (MGD) and R-signature. Then, I describe our second method based on Ridgelet transform.Our second contribution consists in a new hybrid page segmentation approach. I first describe our stroke-based descriptor that allows detecting text and line candidates using the skeleton of the binarized document image. Then, an active contour model is applied to segment the rest of the image into photo and background regions. Finally, text candidates are clustered using mean-shift analysis technique according to their corresponding sizes. The method is applied for segmenting scanned document images (newspapers and magazines) that contain text, lines and photo regions. Finally, I describe our stroke-based text extraction method. Our approach begins by extracting connected components and selecting text character candidates over the CIE LCH color space using the Histogram of Oriented Gradients (HOG) correlation coefficients in order to detect low contrasted regions. The text region candidates are clustered using two different approaches ; a depth first search approach over a graph, and a stable text line criterion. Finally, the resulted regions are refined by classifying the text line candidates into « text» and « non-text » regions using a Kernel Support Vector Machine K-SVM classifier
Mazyad, Ahmad. "Contribution to automatic text classification : metrics and evolutionary algorithms". Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0487/document.
Texto completoThis thesis deals with natural language processing and text mining, at the intersection of machine learning and statistics. We are particularly interested in Term Weighting Schemes (TWS) in the context of supervised learning and specifically the Text Classification (TC) task. In TC, the multi-label classification task has gained a lot of interest in recent years. Multi-label classification from textual data may be found in many modern applications such as news classification where the task is to find the categories that a newswire story belongs to (e.g., politics, middle east, oil), based on its textual content, music genre classification (e.g., jazz, pop, oldies, traditional pop) based on customer reviews, film classification (e.g. action, crime, drama), product classification (e.g. Electronics, Computers, Accessories). Traditional classification algorithms are generally binary classifiers, and they are not suited for the multi-label classification. The multi-label classification task is, therefore, transformed into multiple single-label binary tasks. However, this transformation introduces several issues. First, terms distributions are only considered in relevance to the positive and the negative categories (i.e., information on the correlations between terms and categories is lost). Second, it fails to consider any label dependency (i.e., information on existing correlations between classes is lost). Finally, since all categories but one are grouped into one category (the negative category), the newly created tasks are imbalanced. This information is commonly used by supervised TWS to improve the effectiveness of the classification system. Hence, after presenting the process of multi-label text classification, and more particularly the TWS, we make an empirical comparison of these methods applied to the multi-label text classification task. We find that the superiority of the supervised methods over the unsupervised methods is still not clear. We show then that these methods are not fully adapted to the multi-label classification problem and they ignore much statistical information that coul be used to improve the classification results. Thus, we propose a new TWS based on information gain. This new method takes into consideration the term distribution, not only regarding the positive and the negative categories but also in relevance to all classes. Finally, aiming at finding specialized TWS that also solve the issue of imbalanced tasks, we studied the benefits of using genetic programming for generating TWS for the text classification task. Unlike previous studies, we generate formulas by combining statistical information at a microscopic level (e.g., the number of documents that contain a specific term) instead of using complete TWS. Furthermore, we make use of categorical information such as (e.g., the number of categories where a term occurs). Experiments are made to measure the impact of these methods on the performance of the model. We show through these experiments that the results are positive
Bastos, Dos Santos José Eduardo. "L'identification de texte en images de chèques bancaires brésiliens". Compiègne, 2003. http://www.theses.fr/2003COMP1453.
Texto completoIdentifying and distinguishing text in document images are tasks whose cat!Jal solutions are mainly based on using contextual informations, like layout informations or informations from the phisical structure. Ln this research work, an alternative for this task is investigated based only in features observed from textual elements, giving more independency to the process. The hole process was developped considering textual elements fragmented in sm ail portions(samples) in order to provide an alternative solution to questions Iike scale and textual elements overlapping. From these samples, a set of features is extracted and serves as input to a classifyer maily chrged with textual extraction from the document and also the distinguish between handwritting and machine-printed text. Moreover, sinGe the only informations emplyed is observed directly from textual elements, the process assumes a character more independent as it doesn't use any heuristics nor à priori information of the treated document. Results around 93% of correct classification confirms the efficacy of the process
Libros sobre el tema "Classification texte"
Les mille et une nuits: Histoire du texte et classification des contes. Paris: L'Harmattan, 2008.
Buscar texto completoAnne-Elizabeth, Dalcq, ed. Mettre de l'ordre dans ses idees: Classification des articulations logiques pour structurer son texte. Paris: Duculot, 1999.
Buscar texto completoLes parenthèses dans l'Evangile de Jean: Aperçu historique et classification, texte grec de Jean. Leuven: University Press, 1985.
Buscar texto completoTöppe, Frank. Im Zeichen der drei goldenen Haare: Der Teufel mit den drei goldenen Haaren, der Vogel Greif : die Beziehungen beider Texte zueinander, zu den entsprechenden Texten der Erstauflage der Grimmschen Märchen und einer F. v. Arnimschen Überlieferung. Meerbusch-Büderich bei Düsseldorf: Edition Vogelmann, 1993.
Buscar texto completoDescartes, René. Discours de la méthode: Texte intégral. Paris: Hatier, 1990.
Buscar texto completoQuintilian. De institutione oratoria, liber primus: Texte latin publié avec des notes biographiques sur Quintilien, l'histoire de l'institution oratoire et de ses abrégés, la classification et la description des manuscrits, le texte abrégé par Étienne de Rouen et par Jean Racine. Des notes critiques les variantes principales par Ch. Fierville. Paris: Firmin-Didot, 1991.
Buscar texto completoGenome clustering: From linguistic models to classification of genetic texts. Berlin: Springer, 2010.
Buscar texto completoSupport vector machines for pattern classification. 2a ed. London: Springer, 2010.
Buscar texto completoAlmodóvar, Antonio Rodríguez. El texto infinito: Ensayos sobre el cuento popular. Madrid: Fundación Germán Sánchez Ruipérez, 2004.
Buscar texto completoLos cuentos populares o la tentativa de un texto infinito. [Murcia]: Universidad de Murcia, 1989.
Buscar texto completoCapítulos de libros sobre el tema "Classification texte"
Féron, Corinne. "Classification des adverbiaux du moyen français: l’exemple des expressions formées sur vérité, voir, vrai et certain". En Texte, Codex & Contexte, 123–33. Turnhout: Brepols Publishers, 2007. http://dx.doi.org/10.1484/m.tcc-eb.3.3946.
Texto completoJoachims, Thorsten. "Text Classification". En Learning to Classify Text Using Support Vector Machines, 7–33. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0907-3_2.
Texto completoSarkar, Dipanjan. "Text Classification". En Text Analytics with Python, 167–215. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2388-8_4.
Texto completoAli-Ahmed, Syed Toufeeq. "Text Classification". En Encyclopedia of Systems Biology, 2156. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_174.
Texto completoSarkar, Dipanjan. "Text Classification". En Text Analytics with Python, 275–342. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_5.
Texto completoSahin, Özgür. "Text Classification". En Develop Intelligent iOS Apps with Swift, 41–67. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6421-8_3.
Texto completoZong, Chengqing, Rui Xia y Jiajun Zhang. "Text Classification". En Text Data Mining, 93–124. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0100-2_5.
Texto completoWasserman, Larry. "Classification". En Springer Texts in Statistics, 349–79. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21736-9_22.
Texto completoJames, Gareth, Daniela Witten, Trevor Hastie y Robert Tibshirani. "Classification". En Springer Texts in Statistics, 127–73. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7138-7_4.
Texto completoJames, Gareth, Daniela Witten, Trevor Hastie y Robert Tibshirani. "Classification". En Springer Texts in Statistics, 129–95. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1418-1_4.
Texto completoActas de conferencias sobre el tema "Classification texte"
Yang, Yi, Hongan Wang, Jiaqi Zhu, Yunkun Wu, Kailong Jiang, Wenli Guo y Wandong Shi. "Dataless Short Text Classification Based on Biterm Topic Model and Word Embeddings". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/549.
Texto completoKavi, Deniz. "Towards Adversarial Genetic Text Generation". En 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110407.
Texto completoSouza, Luiz Fernando Spillere de y Alexandre Leopoldo Gonçalves. "UTILIZAÇÃO PRÁTICA DE WORD EMBEDDING APLICADA À CLASSIFICAÇÃO DE TEXTO". En Congresso Internacional de Conhecimento e Inovação (ciKi). Congresso Internacional de Conhecimento e Inovação (ciKi), 2020. http://dx.doi.org/10.48090/ciki.v1i1.899.
Texto completoHuang, Ting, Gehui Shen y Zhi-Hong Deng. "Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/697.
Texto completoChaika, M., I. Buneev y V. Velichko. "ANALYSIS OF THE EFFECTIVENESS OF APPLYING THE FREQUENCY-CONTEXT CLASSIFICATION ALGORITHM TO TEXTS OF DIFFERENT STYLES". En Modern aspects of modeling systems and processes. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2021. http://dx.doi.org/10.34220/mamsp_174-178.
Texto completoOuyang, Jihong, Yiming Wang, Ximing Li y Changchun Li. "Weakly-supervised Text Classification with Wasserstein Barycenters Regularization". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/468.
Texto completoFontanille, Jacques. "La sémiotique est-elle un art ? Le faire sémiotique comme « art libéral »". En Arts du faire : production et expertise. Limoges: Université de Limoges, 2009. http://dx.doi.org/10.25965/as.3343.
Texto completoShamardina, Tatiana, Vladislav Mikhailov, Daniil Chernianskii, Alena Fenogenova, Marat Saidov, Anastasiya Valeeva, Tatiana Shavrina, Ivan Smurov, Elena Tutubalina y Ekaterina Artemova. "Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian". En Dialogue. RSUH, 2022. http://dx.doi.org/10.28995/2075-7182-2022-21-497-511.
Texto completoChen, Jun, Quan Yuan, Chao Lu y Haifeng Huang. "A Novel Sequence-to-Subgraph Framework for Diagnosis Classification". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/496.
Texto completoSubramaniam, Raghav. "Examining Accuracy Heterogeneities in Classification of Multilingual". En 8th International Conference on Software Engineering. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131221.
Texto completoInformes sobre el tema "Classification texte"
Furey, John, Austin Davis y Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/41960.
Texto completoSeo, Young-Woo, Joseph Giampapa y Katia Sycara. Text Classification for Intelligent Portfolio Management. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2002. http://dx.doi.org/10.21236/ada595830.
Texto completoDasigi, V. R., R. C. Mann y V. Protopopescu. Multi-sensor text classification experiments -- a comparison. Office of Scientific and Technical Information (OSTI), enero de 1997. http://dx.doi.org/10.2172/638201.
Texto completoHan, Euihong, George Karypis y Vipin Kumar. Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1999. http://dx.doi.org/10.21236/ada439688.
Texto completoKuzmina, Aleksandra, Amalia Kuregyan y Ekaterina Pertsevaya. PSUDOINTERNATIONAL WORDS IN THE TRANSLATION OF ECONOMIC TEXTS CARRIED OUT BY THE STUDENTS OF NON-LINGUISTIC UNIVERSITIES. Crimean Federal University named after V.I. Vernadsky, 2023. http://dx.doi.org/10.12731/ttxnbz.
Texto completoChew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, Caroline W. Oppenheimer, Brianna D'Arcangelo, Autumn Barnes, Shirley Liu, Adam Bryant Miller, Ashley Lowe y Anna C. Yaros. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, marzo de 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.
Texto completoDasigi, V. R. y R. C. Mann. Toward a multi-sensor-based approach to automatic text classification. Office of Scientific and Technical Information (OSTI), octubre de 1995. http://dx.doi.org/10.2172/130610.
Texto completoDewdney, Nigel, Carol VanEss-Dykema y Richard MacMillan. The Form is the Substance: Classification of Genres in Text. Fort Belvoir, VA: Defense Technical Information Center, enero de 2001. http://dx.doi.org/10.21236/ada460898.
Texto completoPavlicheva, E. N., V. P. Meshalkin y N. S. CHikunov. Algorithm for processing text data for an automatic classification problem using the word2vec method. OFERNIO, febrero de 2021. http://dx.doi.org/10.12731/ofernio.2021.24759.
Texto completoKNYAZEVA, V., A. BILYALOVA y E. IBRAGIMOVA. INTERTEXT AS A LEXICAL AND SEMANTIC TOOL OF SUGGESTION. Science and Innovation Center Publishing House, 2022. http://dx.doi.org/10.12731/2077-1770-2022-14-2-3-39-49.
Texto completo