15 research outputs found

    Embodied Agents:A New Impetus to Humor Research

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    Computational Humor 2012:extended abstacts of the (3rd international) Workshop on Computational Humor

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    Separating content and structure in humor appreciation: The need for a bimodal model and support from research into aesthetics

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    For a long time humor theorists have acknowledged that content and structure of humor (or: joke work vs. tendency, [4]; thematic vs. schematic, [12]; cognitive vs. orectic factors, [3]) have to be distinguished as two different sources of pleasure [6]. Nevertheless, against all evidence, taxonomies of humor are stuck in (serial) unimodal classifications rather than bi- or multimodal models. Intuitive classifications of humor typically distinguish between content classes (e.g., blonde jokes, dead baby jokes, Stalin jokes), neglecting the contributions of structural properties to appreciation of humor. Also rational taxonomies most frequently emphasize content features; e.g., when emotional features like disgust, fear or anger are highlighted in humor

    Technologies That Make You Smile: Adding Humor to Text-Based Applications

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    Quantifying the psychological properties of words

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    This thesis explores the psychological properties of words – the idea that words carry links to additional information beyond their dictionary meaning. It does so by presenting three distinct publications and an applied project, the Macroscope. The published research respectively covers: the modelling of language networks to explain lexical growth; the use of high dimensional vector representations of words to discuss language learning; and the collection of a normative dataset of single word humour ratings. The first publication outlines the use of network science in psycholinguistics. The methodology is discussed, providing clear guidelines on the application of networks when answering psychologically motivated questions. A selection of psychological studies is presented as a demonstration of use cases for networks in cognitive psychology. The second publication uses referent feature norms to represent words in a high dimensional vector space. A correlative link between referent distinctiveness and age of acquisition is proposed. The shape bias literature (the idea that children only pay attention to the shape of objects early on) is evaluated in relation to the findings. The third publication collects and shares a normative dataset of single word humour ratings. Descriptive properties of the dataset are outlined and the potential future use in the field of humour is discussed. Finally, the thesis presents the Macroscope, a collaborative project put together with Li Ying. The Macroscope is an online platform, allowing for easy analysis of the psychological properties of target words. The platform is showcased, and its full functionality is presented, including visualisation examples. Overall, the thesis aims to give researchers all that’s necessary to start working with psychological properties of words – the understanding of network science in psycholinguistics, high dimensional vector spaces, normative datasets and the applied use of all the above through the Macroscope

    Automatic Humor Evaluation

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    Cílem této práce je vytvoření systému pro automatické hodnocení humoru. Systém umožňuje predikovat vtipnost a kategorii pro vstup zadaný v angličtině. Hlavní podstatou je vytvoření klasifikátoru a trénování modelu na vytvořených datových sadách pro získání co nejlepších výsledků. Architektura klasifikátoru je založena na neuronových sítích. Systém zároveň obsahuje webové uživatelské rozhraní pro komunikaci s uživatelem. Výsledek je webová aplikace propojená s klasifikátorem umožňující hodnocení uživatelského vstupu a poskytování zpětné vazby od uživatelů.The aim of this thesis is to create a system for automatic humor evaluation. The system allow to predict humor and category for english input. The main essence is to create a classifier and train the model with the created datasets to get the best possible results. The classifier architecture is based on neural networks. The system also includes a web user interface for communication with the user. The result is a web application linked to a classifier that allows user input to be evaluated and user feedback to be provided.
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