947 resultados para semantic annotation
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El estudio de las relaciones causales y su expresión lingüística ha sido comúnmente estudiado desde diferentes perspectivas en los años recientes. Sin embargo, pocos estudios han intentado combinar diferentes enfoques para establecer el significado de estas relaciones, y han investigado de manera contrastiva las señales usadas para expresarlas. Este trabajo de fin de master es un proyecto para avanzar el conocimiento en este área mediante la investigación de: a) la posibilidad de caracterizar las relaciones causales en diferentes tipos, usando características que combinan un enfoque funcional y cognitivo; b) los tipos de relaciones causales preferidas en los textos expositivos en inglés y sus traducciones al español; c) las expresiones lingüísticas preferidas para expresar dichas relaciones causales en los textos originales en inglés y sus traducciones al español. La metodología usada en esta investigación se basa en la anotación manual de un corpus bilingüe compuesto de un total de 37 textos expositivos (incluyendo los textos originales en inglés y sus traducciones al español) extraídos del corpus MULTINOT, un corpus de alta calidad, con registros diversificados y multifuncional bilingüe inglésespañol, actualmente compilado y anotado multidimensionalmente por los miembros del grupo de investigación FUNCAP con el proyecto MULTINOT (véase Lavid et al.2015) El estudio se llevó a cabo en cuatro pasos principales: primero, un esquema de anotación para las relaciones causales en inglés y español fue diseñado constando de tres sistemas interrelacionados y sus correspondientes características; tras ello, se compiló un inventario de señales para las relaciones causales en inglés y español, y una categorización en diferentes tipos; seguidamente, el esquema de anotación fue implementado en la herramienta UAM Corpus Tool y el conjunto de textos bilingües fue anotado por el autor de este estudio; finalmente, los datos extraídos de la anotación fueron analizados estadísticamente para comprobar las posibles diferencias entre los textos originales en inglés y sus traducciones al español respecto a la selección del tipo de relación de causa y sus señales. El análisis estadístico de los datos anotados sugiere que los tipos de relaciones de causa preferidos en los textos originales en inglés y son los tipos de contenido y no volitivos, que el orden de aparición de estos tipos de señales preferido es la segunda posición, y las señales más recurrentes usadas para expresar dichas relaciones son las conjunciones, seguidas de los sintagmas verbales. El análisis de las traducciones al español revela un alto grado de similitud con los datos de los textos originales en inglés, lo que sugiere que en las traducciones al español se conservan las preferencias de los textos originales en la mayoría de los casos y que estas elecciones pueden considerarse un indicativo de los textos expositivos en inglés. Proyectos futuros se centraran en el análisis de los textos originales en español para comprobar si las tendencias observadas en los textos originales en inglés y sus traducciones al español son también validas en textos originales en español, y en la especificación de patrones que puede ayudar al análisis automático de estas relaciones
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Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1
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Other
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Peer reviewed
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Postprint
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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.
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The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.
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Discussion tools in existing LEs have few or no integrated tools to analyse student learning. This paper proposes tools not only for integrating social network analytics, but also why we need to semantically tag and track key concepts within posts in order to make student learning in discussions visible. This paper will argue for the importance of semantic markup in discussion tools using screenshots of existing LEs and UI mockups of semantically aware discussion tools to argue the case for this element of next generation LEs
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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.
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Studies of Alexander Pope's poetry tend to examine only the footnotes to his Dunciads, if they examine his footnotes at all. This dissertation will address this deficit in our understanding of Pope's poetics through an examination of Pope's use of footnotes in support of his verse throughout his career. With Gerard Genette's taxonomy of footnotes as variously paratext and text and Hugh Kenner's idea of the technological space of the printed page as frameworks within which Pope's footnotes operate, this dissertation will show that – over the course of his career – Pope developed a poetics of annotation that deployed footnotes rhetorically as appeals to ethos and pathos that both built up Pope's own authorial ethos for his audience in the literary market place of early eighteenth-century London and for posterity and used that authorial ethos in support of his literary and political friends.
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Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.
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The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.
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Stimuli that cannot be perceived (i.e., that are subliminal) can still elicit neural responses in an observer, but can such stimuli influence behavior and higher-order cognition? Empirical evidence for such effects has periodically been accepted and rejected over the last six decades. Today, many psychologists seem to consider such effects well-established and recent studies have extended the power of subliminal processing to new limits. In this thesis, I examine whether this shift in zeitgeist is matched by a shift in evidential strength for the phenomenon. This thesis consists of three empirical studies involving more than 250 participants, a simulation study, and a quantitative review. The conclusion based on these efforts is that several methodological, statistical, and theoretical issues remain in studies of subliminal processing. These issues mean that claimed subliminal effects might be caused by occasional or weak percepts (given the experimenters’ own definitions of perception) and that it is still unclear what evidence there is for the cognitive processing of subliminal stimuli. New data are presented suggesting that even in conditions traditionally claimed as “subliminal”, occasional or weak percepts may in fact influence cognitive processing more strongly than do the physical stimuli, possibly leading to reversed priming effects. I also summarize and provide methodological, statistical, and theoretical recommendations that could benefit future research aspiring to provide solid evidence for subliminal cognitive processing.