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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 Page: 1 2 3 4 5...19 Hits 1 – 20 of 368 1 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios Eskander, Ramy. - 2021 BASE Show details 2 Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning Mozafari, Marzieh. - : HAL CCSD, 2021 In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021) BASE Show details 3 Time-locked Cortical Processing of Speech in Complex Environments ... Kulasingham, Joshua Pranjeevan. - : Digital Repository at the University of Maryland, 2021 BASE Show details 4 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios ... Eskander, Ramy. - : Columbia University, 2021 BASE Show details 5 Discriminative feature modeling for statistical speech recognition ... Tüske, Zoltán. - : RWTH Aachen University, 2021 BASE Show details 6 Parlez-vous le hate?: Examining topics and hate speech in the alternative social network Parler Ward, Ethan. - : University of Waterloo, 2021 BASE Show details 7 ASR and Human Recognition Errors: Predictability and Lexical Factors Mansfield, Courtney S. - 2021 BASE Show details 8 Marqueurs discursifs de neurodégénérescence liée à la pathologie Alzheimer Slegers, Antoine. - 2021 BASE Show details 9 Time-locked Cortical Processing of Speech in Complex Environments Kulasingham, Joshua Pranjeevan. - 2021 BASE Show details 10 Crowdsourcing linguistic resources for natural non-standardised languages processing ; Myriadisation de ressources linguistiques pour le traitement automatique de langues non standardisées Millour, Alice. - : HAL CCSD, 2020 In: https://hal.archives-ouvertes.fr/tel-03083213 ; Informatique et langage [cs.CL]. Sorbonne Universite, 2020. Français (2020) BASE Show details 11 Parkinson's desease detection by multimodal analysis combining handwriting and speech signals ; Détection de la maladie de Parkinson par analyse multimodale combinant signaux d’écriture et de parole Taleb, Catherine. - : HAL CCSD, 2020 In: https://tel.archives-ouvertes.fr/tel-03594895 ; Signal and Image Processing. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAT039⟩ (2020) BASE Show details 12 Comment parle un robot ? ; Comment parle un robot ?: Les machines à langage dans la science-fiction Landragin, Frédéric. - : HAL CCSD, 2020. : Le Bélial', 2020 In: https://hal.archives-ouvertes.fr/hal-02548113 ; Le Bélial', 2020, Collection Parallaxe, 978-2-84344-965-9 ; https://www.belial.fr/ (2020) BASE Show details 13 The Effects of Prediction and Speech Rate on Lexical Processing ... Cole, Alissa. - : Digital Repository at the University of Maryland, 2020 BASE Show details 14 Analysis of Speech Parameters as Indicators of Engagement in Conversation ELIAS, CHRISTY. - : Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Science, 2020 BASE Show details 15 Uncovering the effects of semantic context on the cortical processing of continuous speech using computational models of language BRODERICK, MICHAEL. - : Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2020 BASE Show details 16 Investigating the Neural Correlates of Speech Processing & Selective Auditory Attention using EEG TEOH, EMILY SIEW. - : Trinity College Dublin. School of Engineering. Discipline of Electronic & Elect. Engineering, 2020 BASE Show details 17 Demographic-Aware Natural Language Processing Garimella, Aparna. - 2020 Abstract: The underlying traits of our demographic group affect and shape our thoughts, and therefore surface in the way we express ourselves and employ language in our day-to-day life. Understanding and analyzing language use in people from different demographic backgrounds help uncover their demographic particularities. Conversely, leveraging these differences could lead to the development of better language representations, thus enabling further demographic-focused refinements in natural language processing (NLP) tasks. In this thesis, I employ methods rooted in computational linguistics to better understand various demographic groups through their language use. The thesis makes two main contributions. First, it provides empirical evidence that words are indeed used differently by different demographic groups in naturally occurring text. Through experiments conducted on large datasets which display usage scenarios for hundreds of frequent words, I show that automatic classification methods can be effective in distinguishing between word usages of different demographic groups. I compare the encoding ability of the utilized features by conducting feature analyses, and shed light on how various attributes contribute to highlighting the differences. Second, the thesis explores whether demographic differences in word usage by different groups can inform the development of more refined approaches to NLP tasks. Specifically, I start by investigating the task of word association prediction. The thesis shows that going beyond the traditional ``one-size-fits-all'' approach, demographic-aware models achieve better performances in predicting word associations for different demographic groups than generic ones. Next, I investigate the impact of demographic information on part-of-speech tagging and syntactic parsing, and the experiments reveal numerous part-of-speech tags and syntactic relations, whose predictions benefit from the prevalence of a specific group in the training data. Finally, I explore demographic-specific humor generation, and develop a humor generation framework to fill-in the blanks to generate funny stories, while taking into account people's demographic backgrounds. ; PHD ; Computer Science & Engineering ; University of Michigan, Horace H. Rackham School of Graduate Studies ; https://deepblue.lib.umich.edu/bitstream/2027.42/155164/1/gaparna_1.pdf Keyword: Computer Science; Demographic-Aware Humor Generation in Mad Libs; Demographic-Aware Natural Language Processing; Demographic-Aware Word Associations; Engineering; Gender-Bias in Part-of-Speech Tagging and Dependency Parsing; Identifying Demographic Differences in Word Usage; Personalization in Language URL: https://hdl.handle.net/2027.42/155164 BASE Hide details 18 Robust Methods for the Automatic Quantification and Prediction of Affect in Spoken Interactions Aldeneh, Zakaria. - 2020 BASE Show details 19 Neural Correlates of Phonetic and Lexical Processing in Children with and without Speech Sound Disorder Katelyn L Gerwin (8968220). - 2020 BASE Show details 20 Processamento de fala para triagem de distúrbios fonológicos ; Speech processing for screening off phonological disorders Yoshimura, Guilherme Jun. - : Biblioteca Digital de Teses e Dissertações da USP, 2020. : Universidade de São Paulo, 2020. : Instituto de Matemática e Estatística, 2020 BASE Show details Page: 1 2 3 4 5...19

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