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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 Abstract: With the high cost of manually labeling data and the increasing interest in low-resource languages, for which human annotators might not be even available, unsupervised approaches have become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this work, we propose new fully unsupervised approaches for two tasks in morphology: unsupervised morphological segmentation and unsupervised cross-lingual part-of-speech (POS) tagging, which have been two essential subtasks for several downstream NLP applications, such as machine translation, speech recognition, information extraction and question answering. We propose a new unsupervised morphological-segmentation approach that utilizes Adaptor Grammars (AGs), nonparametric Bayesian models that generalize probabilistic context-free grammars (PCFGs), where a PCFG models word structure in the task of morphological segmentation. We implement the approach as a publicly available morphological-segmentation framework, MorphAGram, that enables unsupervised morphological segmentation through the use of several proposed language-independent grammars. In addition, the framework allows for the use of scholar knowledge, when available, in the form of affixes that can be seeded into the grammars. The framework handles the cases when the scholar-seeded knowledge is either generated from language resources, possibly by someone who does not know the language, as weak linguistic priors, or generated by an expert in the underlying language as strong linguistic priors. Another form of linguistic priors is the design of a grammar that models language-dependent specifications. We also propose a fully unsupervised learning setting that approximates the effect of scholar-seeded knowledge through self-training. Moreover, since there is no single grammar that works best across all languages, we propose an approach that picks a nearly optimal configuration (a learning setting and a grammar) for an unseen language, a language that is not part of the development. Finally, we examine multilingual learning for unsupervised morphological segmentation in low-resource setups. For unsupervised POS tagging, two cross-lingual approaches have been widely adapted: 1) annotation projection, where POS annotations are projected across an aligned parallel text from a source language for which a POS tagger is accessible to the target one prior to training a POS model; and 2) zero-shot model transfer, where a model of a source language is directly applied on texts in the target language. We propose an end-to-end architecture for unsupervised cross-lingual POS tagging via annotation projection in truly low-resource scenarios that do not assume access to parallel corpora that are large in size or represent a specific domain. We integrate and expand the best practices in alignment and projection and design a rich neural architecture that exploits non-contextualized and transformer-based contextualized word embeddings, affix embeddings and word-cluster embeddings. Additionally, since parallel data might be available between the target language and multiple source ones, as in the case of the Bible, we propose different approaches for learning from multiple sources. Finally, we combine our work on unsupervised morphological segmentation and unsupervised cross-lingual POS tagging by conducting unsupervised stem-based cross-lingual POS tagging via annotation projection, which relies on the stem as the core unit of abstraction for alignment and projection, which is beneficial to low-resource morphologically complex languages. We also examine morpheme-based alignment and projection, the use of linguistic priors towards better POS models and the use of segmentation information as learning features in the neural architecture. We conduct comprehensive evaluation and analysis to assess the performance of our approaches of unsupervised morphological segmentation and unsupervised POS tagging and show that they achieve the state-of-the-art performance for the two morphology tasks when evaluated on a large set of languages of different typologies: analytic, fusional, agglutinative and synthetic/polysynthetic. Keyword: Automatic speech recognition--Computer programs; Computer science; Machine translating; Question-answering systems; Speech processing systems--Computer programs URL: https://doi.org/10.7916/d8-jd2d-9p51 BASE Hide 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 BASE Show 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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