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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...147 Hits 1 – 20 of 2.933 1 Text as data : computational methods of understanding written expression using SAS Bawa, Gurpreet Singh; De Ville, Barry. - New York : Wiley, 2022 BLLDB UB Frankfurt Linguistik Show details 2 An investigation of English-Irish machine translation and associated resources Dowling, Meghan. - : Dublin City University. School of Computing, 2022. : Dublin City University. ADAPT, 2022 In: Dowling, Meghan orcid:0000-0003-1637-4923 (2022) An investigation of English-Irish machine translation and associated resources. PhD thesis, Dublin City University. (2022) BASE Show details 3 Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache ... Weigelt, Sebastian. - : KIT Scientific Publishing, 2022 BASE Show details 4 The First Gospel, the Gospel of the Poor: A New Reconstruction of Q and Resolution of the Synoptic Problem based on Marcion's Early Luke ... Bilby, Mark G.. - : Zenodo, 2022 BASE Show details 5 The First Gospel, the Gospel of the Poor: A New Reconstruction of Q and Resolution of the Synoptic Problem based on Marcion's Early Luke ... Bilby, Mark G.. - : Zenodo, 2022 BASE Show details 6 Found speech and humans in the loop : Ways to gain insight into large quantities of speech Fallgren, Per. - : KTH, Tal-kommunikation, 2022 Abstract: Found data - data used for something other than the purpose for which it was originally collected - holds great value in many regards. It typically reflects high ecological validity, a strong cultural worth, and there are significant quantities at hand. However, it is noisy, hard to search through, and its contents are often largely unknown. This thesis explores ways to gain insight into such data collections, specifically with regard to speech and audio data. In recent years, deep learning approaches have shown unrivaled performance in many speech and language technology tasks. However, in addition to large datasets, many of these methods require vast quantities of high-quality labels, which are costly to produce. Moreover, while there are exceptions, machine learning models are typically trained for solving well-defined, narrow problems and perform inadequately in tasks of more general nature - such as providing a high-level description of the contents in a large audio file. This observation reveals a methodological gap that this thesis aims to fill. An ideal system for tackling these matters would combine humans' flexibility and general intelligence with machines' processing power and pattern-finding capabilities. With this idea in mind, the thesis explores the value of including the human-in-the-loop, specifically in the context of gaining insight into collections of found speech. The aim is to combine techniques from speech technology, machine learning paradigms, and human-in-the-loop approaches, with the overall goal of developing and evaluating novel methods for efficiently exploring large quantities of found speech data. One of the main contributions is Edyson, a tool for fast browsing, exploring, and annotating audio. It uses temporally disassembled audio, a technique that decouples the audio from the temporal dimension, in combination with feature extraction methods, dimensionality reduction algorithms, and a flexible listening function, which allows a user to get an informative overview of the contents. Furthermore, crowdsourcing is explored in the context of large-scale perception studies and speech & language data collection. Prior reports on the usefulness of crowd workers for such tasks show promise and are here corroborated. The thesis contributions suggest that the explored approaches are promising options for utilizing large quantities of found audio data and deserve further consideration in research and applied settings. ; Funnet data - data som används för något annat än det syfte som det först samlades in för - är värdefullt i många avseenden. Det reflekterar vanligtvis hög ekologisk validitet, det har ett starkt kulturellt värde, och det finns stora mängder att ta del av. Det är dock fyllt av brus, svårt att få en överblick av, och ofta är innehållet inte tydligt. Denna avhandling utforskar metoder som ger insikt i dessa datasamlingar, specifikt vad gäller tal och ljud. På senare tid har djupinlärning producerat oöverträffade resultat inom tal och språkteknologi. Många av dessa metoder behöver dock väldiga mängder annoterat data, vilket är kostsamt att skapa. Dessutom är maskininlärningsmodeller vanligtvis tränade med väldefinierade problem i åtanke, och presterar sämre inom mer generella uppgifter - såsom att tillhandahålla en övergripande beskrivning av innehållet i en stor ljudfil. Denna observation visar på en brist inom existerande metodologier, således finns det ett behov av vidare tekniker vilket är något som denna avhandling syftar till att täcka. Ett idealt angreppsätt för dessa problem kombinerar flexibiliteten och den generella intelligensen hos en människa med beräkningskraften och mönsterigenkänningsförmågan hos en maskin. Utifrån dessa idéer utforskar avhandlingen värdet av att inkludera människan i loopen, specifikt utifrån hur insikter om stora insamlingar av funnet tal kan skapas. Huvudidén är således att kombinera tekniker från talteknologi, maskininlärningsparadigm, samt människa-i-loopen-metoder, med det övergripande målet att utveckla och utvärdera nya metoder för utforskandet av stora mängder funnet taldata. Ett primärt bidrag är Edyson, ett verktyg för snabb genomlyssning och annotering av ljud. Det bygger på tidsmässig isärtagning av ljud i kombination med särdragsextraktion, dimensionsreduceringsalgoritmer, samt en flexibel lyssningsfunktion, vilket ger en användare en informativ överblick av innehållet. Vidare undersöks crowdsourcing inom kontexten av storskaliga perceptionsstudier och datainsamling av tal och språkdata. Tidigare rapporter som visar på användbarheten av crowd workers är styrkta av avhandlingens bidrag. Avhandlingsbidragen visar att de undersökta metoderna är lovande alternativ för utforskandet av stora mängder funnet ljuddata och förtjänar vidare uppmärksamhet. ; QC 20220222 Keyword: crowdsourcing; dimensionality reduction; Found data; found speech; human-in-the-loop; Language Technology (Computational Linguistics); sound browsing; Språkteknologi (språkvetenskaplig databehandling); visualization URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-309031 BASE Hide details 7 Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture Crater, David T. - 2022 BASE Show details 8 Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache Weigelt, Sebastian. - : KIT Scientific Publishing, Karlsruhe, 2022 BASE Show details 9 CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse Rüdiger, Jan Oliver. - 2022 BASE Show details 10 Sentence comprehension as a cognitive process : a computational approach Vasishth, Shravan [Verfasser]; Engelmann, Felix [Verfasser]. - Cambridge : Cambridge University Press, 2021 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 11 English machine reading comprehension: new approaches to answering multiple-choice questions Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021 In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021) BASE Show details 12 Proactive information retrieval Sen, Procheta. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021 In: Sen, Procheta (2021) Proactive information retrieval. PhD thesis, Dublin City University. (2021) BASE Show details 13 Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics: Dagstuhl Seminar 21351 Baldwin, Timothy; Croft, William; Nivre,, Joakim. - : HAL CCSD, 2021 In: Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03507948 ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics, Aug 2021, pp.89--138, 2021, 2192-5283. ⟨10.4230/DagRep.11.7.89⟩ ; https://gitlab.com/unlid/dagstuhl-seminar/-/wikis/home (2021) BASE Show details 14 Type-logical investigations: proof-theoretic, computational and linguistic aspects of modern type-logical grammars Moot, Richard. - : HAL CCSD, 2021 In: https://hal-lirmm.ccsd.cnrs.fr/tel-03452731 ; Computation and Language [cs.CL]. Université Montpellier, 2021 (2021) BASE Show details 15 Weak supervision for learning discourse structure in multi-party dialogues ; Supervision distante pour l'apprentissage de structures discursives dans les conversations multi-locuteurs Badene, Sonia. - : HAL CCSD, 2021 In: https://tel.archives-ouvertes.fr/tel-03622653 ; Artificial Intelligence [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. English. ⟨NNT : 2021TOU30138⟩ (2021) BASE Show details 16 Dialogue Modeling in a Dynamic Framework ; Modélisation dynamique des dialogues Boritchev, Maria. - : HAL CCSD, 2021 In: https://hal.archives-ouvertes.fr/tel-03541628 ; Computation and Language [cs.CL]. Université de Lorraine; École doctorale IAEM Lorraine - Informatique, Automatique, Électronique - Électrotechnique, Mathématiques de Lorraine, 2021. English. ⟨NNT : 2021LORR0199⟩ (2021) BASE Show details 17 CorpusExplorer ... : Eine Software zur korpuspragmatischen Analyse ... Rüdiger, Jan Oliver. - : Universität Kassel, 2021 BASE Show details 18 Modelowanie percepcji transformacji politycznej. Podejście systemowe ... Kasianiuk, Krzysztof. - : figshare, 2021 BASE Show details 19 Modelowanie percepcji transformacji politycznej. Podejście systemowe ... Kasianiuk, Krzysztof. - : figshare, 2021 BASE Show details 20 Sources Matter: A Comparison of Fake News Datasets on Linguistic Feature Performance ... Wang, Miaohan. - : University of Chicago, 2021 BASE Show details Page: 1 2 3 4 5...147

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