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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; Fornaciari, Tommaso; Miller, Tristan; Plank, Barbara; Poesio, Massimo; Simpson, Edwin; Uma, Alexandra. - : Underline Science Inc., 2021 Abstract: Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assumethat a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on Learning with Disagreements (Le-wi-Di) wasto provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/30027-semeval-2021-task-12-learning-with-disagreement https://dx.doi.org/10.48448/jnrq-9d84 BASE Hide 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. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2020 BASE Show 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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