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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 SemEval 2021 Task 12: Learning with Disagreement ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chamberlain, Jon; Dumitrache, Anca. - : Underline Science Inc., 2021 BASE Show details 2 SemEval-2021 Task 12: Learning with Disagreements Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021 BASE Show details 3 Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ... Miller, Tristan; Dinh, Erik-Lân Do; Simpson, Edwin. - : arXiv, 2020 BASE Show details 4 Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ; Identificando el humor de tuits utilizando el aprendizaje de preferencias basado en procesos gaussianos Miller, Tristan; Do Dinh, Erik-Lân; Simpson, Edwin; Gurevych, Iryna. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2020 Abstract: Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum. In this paper, we present a probabilistic approach, a variant of Gaussian process preference learning (GPPL), that learns to rank and rate the humorousness of short texts by exploiting human preference judgments and automatically sourced linguistic annotations. We apply our system, which is similar to one that had previously shown good performance on English-language one-liners annotated with pairwise humorousness annotations, to the Spanish-language data set of the HAHA@IberLEF2019 evaluation campaign. We report system performance for the campaign's two subtasks, humour detection and funniness score prediction, and discuss some issues arising from the conversión between the numeric scores used in the HAHA@IberLEF2019 data and the pairwise judgment annotations required for our method. ; Actualmente la mayoría de los sistemas de procesamiento de humor hacen, en el mejor de los casos, distinciones discretas y granualeres entre lo cómico y lo convencional. Sin embargo, dichos conceptos se conciben mejor en un espectro más amplio. Este artículo presenta un método probabilístico, un modo de preferencias de aprendizaje basadas en un proceso gaussiano (GPPL), que aprende a clasificar y calificar el humor de textos cortos explotando juicios de preferencia humana y anotaciones lingüísticas generadas en forma automática. Nuestro sistema es similar a uno que previamente había demostrado un buen desempeño en frases en inglés anotadas con anotaciones humorísticas por pares y lo aplicamos a la colección de datos en español de la campaña de evaluación HAHA@IberLEF2019. En este trabajo reportamos el desempeño del sistema para dos subtareas de la campaña: detección de humor y predicción de puntaje de diversión. También presentamos algunos problemas que surgen de la conversión entre los puntajes numéricos utilizados en los datos HAHA@IberLEF2019 y las anotaciones de juicio de pares de documentos requeridas para nuestro método. ; This work has been supported by the German Federal Ministry of Education and Research (BMBF) under the promotional reference 01UG1816B (CEDIFOR), by the German Research Foundation (DFG) as part of the QA-EduInf project (grants GU798/18-1 and RI 803/12-1), by the DFG-funded research training group “Adaptive Preparation of Information from Heterogeneous Sources” (AIPHES; GRK1994/1), and by the Austrian Science Fund (FWF) under project M2625-N31. The Austrian Research Institute for Artificial Intelligence is supported by the Austrian Federal Ministry for Science, Research and Economy. Keyword: Best-worst scaling; Computational humour; Gaussian process preference learning; GPPL; Humor; Humor computacional; Humour; Lenguajes y Sistemas Informáticos; Mejor-peor escala; Preferencias de aprendizaje; Proceso gaussiano URL: http://hdl.handle.net/10045/104712 https://doi.org/10.26342/2020-64-4 BASE Hide details 5 Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning Simpson, Edwin; Venanzi, Matteo; Reece, Steven. - 2015 BASE Show details 6 Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses Simpson, Edwin; Reece, Steven; Penta, Antonio... In: DTIC (2013) BASE Show details

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