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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 Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques ... 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; Bigham, Jeffrey; Khosla, Sopan. - : Underline Science Inc., 2021 BASE Show details 2 Combining Independent Modules in Lexical Multiple-Choice Problems ... Turney, Peter D.; Littman, Michael L.; Bigham, Jeffrey. - : arXiv, 2005 BASE Show details 3 Combining Independent Modules in Lexical Multiple-Choice Problems Turney, Peter D.; Littman, Michael L.; Bigham, Jeffrey. - : John Benjamins, 2004 BASE Show details 4 Combining independent modules to solve multiple-choice synonym and analogy problems Turney, Peter; Littman, Michael; Bigham, Jeffrey. - 2003 BASE Show details 5 Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems ... Turney, Peter D.; Littman, Michael L.; Bigham, Jeffrey. - : arXiv, 2003 BASE Show details 6 Predicting risk of dyslexia with an online gamified test Rello, Luz, 1984-; Baeza Yates, Ricardo; Ali, Abdullah; Bigham, Jeffrey P.; Serra Burriel, Miquel. - : Public Library of Science (PLoS) Abstract: Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people. ; Financial support was provided by a grant from the US Department of 249 Education NIDRR (grant number H133A130057, J.B., https://www.ed.gov/); and a 250 grant from the National Science Foundation (grant number IIS-1618784, J.B. and L.R., 251 https://www.nsf.gov/). Keyword: Attention; Dyslexia; Machine learning; Phonology; Semantics; Sytnax; Vision; Working memory URL: http://hdl.handle.net/10230/46999 https://doi.org/10.1371/journal.pone.0241687 BASE Hide details

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