1,197 research outputs found

    Document image classification combining textual and visual features.

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    This research contributes to the problem of classifying document images. The main addition of this thesis is the exploitation of textual and visual features through an approach that uses Convolutional Neural Networks. The study uses a combination of Optical Character Recognition and Natural Language Processing algorithms to extract and manipulate relevant text concepts from document images. Such content information are embedded within document images, with the aim of adding elements which help to improve the classification results of a Convolutional Neural Network. The experimental phase proves that the overall document classification accuracy of a Convolutional Neural Network trained using these text-augmented document images, is considerably higher than the one achieved by a similar model trained solely on classic document images, especially when different classes of documents share similar visual characteristics. The comparison between our method and state-of-the-art approaches demonstrates the effectiveness of combining visual and textual features. Although this thesis is about document image classification, the idea of using textual and visual features is not restricted to this context and comes from the observation that textual and visual information are complementary and synergetic in many aspects

    Multimodal Emotion Classification

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    Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98\% is achieved with a training data of 550k posts.Comment: Accepted at the 2nd Emoji Workshop co-located with The Web Conference 201

    Through the Lens of EAAT Facility Manager: Benefits of Equine-Assisted Activities and Therapies to College-aged students

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    This study aimed to explore and gather information on the benefits of equine assisted activities and therapies (EAAT) to college-aged students so that the information may be given to college students as an educational source and a mental health relief resource. This study strived to explore, through the lens of managers of EAAT organizations and their coworkers, how EAAT has positively affected college-aged students, including those with PTSD, behavioral problems, communication obstacles, Down syndrome, family differences, abusive relationships, depression, anxiety, and/or physical ailments. As a whole, EAAT is viewed as more of a recreational activity rather than a method of therapy. Insurance companies do not provide funds that completely cover the cost of sessions. Not having funds for EAAT leads to many not being able to choose it as an option for treatment. Even though insurance may pay for the mental health professional during the session, it does not cover the outside costs associated with EAAT. This creative project highlighted stories as reported by EAAT professionals that revealed the positive impact that EAAT can have on college-aged students and people of all ages and walks of life. This information was presented in a format that can be used by college-aged students to introduce the benefits of EAAT as they walk through the mental health challenges and the typical stressors experienced by this age grou

    Deliverable D9.3 Final Project Report

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    This document comprises the final report of LinkedTV. It includes a publishable summary, a plan for use and dissemination of foreground and a report covering the wider societal implications of the project in the form of a questionnaire

    Visual Models for Social Media Image Analysis: Groupings, Engagement, Trends, and Rankings

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    With social media image analysis, one collects and interprets online images for the study of topical affairs. This analytical undertaking requires formats for displaying collections of images that enable their inspection. First, we discuss features of social media images to make a case for studying them in groups (rather than individually): multiplicity, circulation, modification, networkedness, and platform specificity. In all, these offer reasons and means for an approach to social media image research that privileges the collection of images as its analytical object. Second, taking the 2019 Amazon rainforest fires as a case study, we present four visual models for analyzing collections of social media images. Each visual model matches a distinctive spatial arrangement with a type of analysis: grouping images by theme with clusters, surfacing dominant images and their engagement with treemaps, following image trends with plots, and comparing image rankings across platforms with grids
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