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Дисертації з теми "Low resource language"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 29 липня 2025

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1

Jansson, Herman. "Low-resource Language Question Answering Systemwith BERT." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42317.

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The complexity for being at the forefront regarding information retrieval systems are constantly increasing. Recent technology of natural language processing called BERT has reached superhuman performance in high resource languages for reading comprehension tasks. However, several researchers has stated that multilingual model’s are not enough for low-resource languages, since they are lacking a thorough understanding of those languages. Recently, a Swedish pre-trained BERT model has been introduced which is trained on significantly more Swedish data than the multilingual models currently avai

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2

Zhang, Yuan Ph D. Massachusetts Institute of Technology. "Transfer learning for low-resource natural language analysis." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108847.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 131-142).<br>Expressive machine learning models such as deep neural networks are highly effective when they can be trained with large amounts of in-domain labeled training data. While such annotations may not be readily avail

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3

Zouhair, Taha. "Automatic Speech Recognition for low-resource languages using Wav2Vec2 : Modern Standard Arabic (MSA) as an example of a low-resource language." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37702.

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The need for fully automatic translation at DigitalTolk, a Stockholm-based company providing translation services, leads to exploring Automatic Speech Recognition as a first step for Modern Standard Arabic (MSA). Facebook AI recently released a second version of its Wav2Vec models, dubbed Wav2Vec 2.0, which uses deep neural networks and provides several English pretrained models along with a multilingual model trained in 53 different languages, referred to as the Cross-Lingual Speech Representation (XLSR-53). The small English and the XLSR-53 pretrained models are tested, and the results stemm

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4

Packham, Sean. "Crowdsourcing a text corpus for a low resource language." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20436.

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Low resourced languages, such as South Africa's isiXhosa, have a limited number of digitised texts, making it challenging to build language corpora and the information retrieval services, such as search and translation that depend on them. Researchers have been unable to assemble isiXhosa corpora of sufficient size and quality to produce working machine translation systems and it has been acknowledged that there is little to know training data and sourcing translations from professionals can be a costly process. A crowdsourcing translation game which paid participants for their contributions w

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5

Louvan, Samuel. "Low-Resource Natural Language Understanding in Task-Oriented Dialogue." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/333813.

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Task-oriented dialogue (ToD) systems need to interpret the user's input to understand the user's needs (intent) and corresponding relevant information (slots). This process is performed by a Natural Language Understanding (NLU) component, which maps the text utterance into a semantic frame representation, involving two subtasks: intent classification (text classification) and slot filling (sequence tagging). Typically, new domains and languages are regularly added to the system to support more functionalities. Collecting domain-specific data and performing fine-grained annotation of large amou

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6

Lakew, Surafel Melaku. "Multilingual Neural Machine Translation for Low Resource Languages." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257906.

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Machine Translation (MT) is the task of mapping a source language to a target language. The recent introduction of neural MT (NMT) has shown promising results for high-resource language, however, poorly performing for low-resource language (LRL) settings. Furthermore, the vast majority of the 7, 000+ languages around the world do not have parallel data, creating a zero-resource language (ZRL) scenario. In this thesis, we present our approach to improving NMT for LRL and ZRL, leveraging a multilingual NMT modeling (M-NMT), an approach that allows building a single NMT to translate across mu

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7

Lakew, Surafel Melaku. "Multilingual Neural Machine Translation for Low Resource Languages." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257906.

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Анотація:

Machine Translation (MT) is the task of mapping a source language to a target language. The recent introduction of neural MT (NMT) has shown promising results for high-resource language, however, poorly performing for low-resource language (LRL) settings. Furthermore, the vast majority of the 7, 000+ languages around the world do not have parallel data, creating a zero-resource language (ZRL) scenario. In this thesis, we present our approach to improving NMT for LRL and ZRL, leveraging a multilingual NMT modeling (M-NMT), an approach that allows building a single NMT to translate across mu

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8

Mairidan, Wushouer. "Pivot-Based Bilingual Dictionary Creation for Low-Resource Languages." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199441.

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9

Samson, Juan Sarah Flora. "Exploiting resources from closely-related languages for automatic speech recognition in low-resource languages from Malaysia." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM061/document.

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Анотація:

Les langues en Malaisie meurent à un rythme alarmant. A l'heure actuelle, 15 langues sont en danger alors que deux langues se sont éteintes récemment. Une des méthodes pour sauvegarder les langues est de les documenter, mais c'est une tâche fastidieuse lorsque celle-ci est effectuée manuellement.Un système de reconnaissance automatique de la parole (RAP) serait utile pour accélérer le processus de documentation de ressources orales. Cependant, la construction des systèmes de RAP pour une langue cible nécessite une grande quantité de données d'apprentissage comme le suggèrent les techniques act

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10

Tafreshi, Shabnam. "Cross-Genre, Cross-Lingual, and Low-Resource Emotion Classification." Thesis, The George Washington University, 2021. http://pqdtopen.proquest.com/#viewpdf?dispub=28088437.

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Emotions can be defined as a natural, instinctive state of mind arising from one’s circumstances, mood, and relationships with others. It has long been a question to be answered by psychology that how and what is it that humans feel. Enabling computers to recognize human emotions has been an of interest to researchers since 1990s (Picard et al., 1995). Ever since, this area of research has grown significantly and emotion detection is becoming an important component in many natural language processing tasks. Several theories exist for defining emotions and are chosen by researchers according to

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11

Singh, Mittul [Verfasser], and Dietrich [Akademischer Betreuer] Klakow. "Handling long-term dependencies and rare words in low-resource language modelling / Mittul Singh ; Betreuer: Dietrich Klakow." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2017. http://d-nb.info/1141677962/34.

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12

Riabi, Arij. "Small is Beautiful : addressing resource scarcity, language variation, and transfer challenges for automatic detection of Harmful language." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS049.

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Les plateformes en ligne sont devenues des espaces majeurs de discussion publique et, à ce titre, favorisent de nombreuses interactions positives. Cependant, ces forums peuvent aussi promouvoir des contenus nuisibles ou radicaux. La montée des abus en ligne, bien qu'ils ne représentent qu'une fraction des communications sur internet, se manifestent sous diverses formes, notamment par des comportements agressifs. L'extrémisme en ligne est devenu l'un des défis les plus pressants découlant de ces comportements agressifs, car il propage non seulement des idéologies nuisibles, mais conduit aussi à

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13

Dyer, Andrew. "Low Supervision, Low Corpus size, Low Similarity! Challenges in cross-lingual alignment of word embeddings : An exploration of the limitations of cross-lingual word embedding alignment in truly low resource scenarios." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395946.

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Cross-lingual word embeddings are an increasingly important reseource in cross-lingual methods for NLP, particularly for their role in transfer learning and unsupervised machine translation, purportedly opening up the opportunity for NLP applications for low-resource languages. However, most research in this area implicitly expects the availablility of vast monolingual corpora for training embeddings, a scenario which is not realistic for many of the world's languages. Moreover, much of the reporting of the performance of cross-lingual word embeddings is based on a fairly narrow set of mostl

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14

Arbi, Haza Nasution. "Bilingual Lexicon Induction Framwork for Closely Related Languages." Kyoto University, 2018. http://hdl.handle.net/2433/235115.

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15

Godard, Pierre. "Unsupervised word discovery for computational language documentation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS062/document.

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La diversité linguistique est actuellement menacée : la moitié des langues connues dans le monde pourraient disparaître d'ici la fin du siècle. Cette prise de conscience a inspiré de nombreuses initiatives dans le domaine de la linguistique documentaire au cours des deux dernières décennies, et 2019 a été proclamée Année internationale des langues autochtones par les Nations Unies, pour sensibiliser le public à cette question et encourager les initiatives de documentation et de préservation. Néanmoins, ce travail est coûteux en temps, et le nombre de linguistes de terrain, limité. Par conséque

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16

Muller, Benjamin. "How Can We Make Language Models Better at Handling the Diversity and Variability of Natural Languages ?" Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS399.

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Ces dernières années, le passage à l’échelle (scaling) des modèles de langues basés sur l’apprentissage profond — principalement en termes de taille de modèle, de taille de l’ensemble de données d’entraînement et de puissance de calcul d’entraînement — est devenu l’une des principales forces motrices des progrès empiriques en Traitement Automatique du Langage (TAL). Comme l’illustrent les exemples de (Peters et al., 2018b; Devlin et al., 2018a; Brown et al., 2020;Zhang et al., 2022; Chowdhery et al., 2022), cela conduit à de meilleures performances en apprentissage supervisé ainsi qu’à de meil

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17

Black, Kevin P. "Interactive Machine Assistance: A Case Study in Linking Corpora and Dictionaries." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5620.

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Machine learning can provide assistance to humans in making decisions, including linguistic decisions such as determining the part of speech of a word. Supervised machine learning methods derive patterns indicative of possible labels (decisions) from annotated example data. For many problems, including most language analysis problems, acquiring annotated data requires human annotators who are trained to understand the problem and to disambiguate among multiple possible labels. Hence, the availability of experts can limit the scope and quantity of annotated data. Machine-learned pre-annotation

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18

Meftah, Sara. "Neural Transfer Learning for Domain Adaptation in Natural Language Processing." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG021.

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Les méthodes d’apprentissage automatique qui reposent sur les Réseaux de Neurones (RNs) ont démontré des performances de prédiction qui s'approchent de plus en plus de la performance humaine dans plusieurs applications du Traitement Automatique de la Langue (TAL) qui bénéficient de la capacité des différentes architectures des RNs à généraliser à partir des régularités apprises à partir d'exemples d'apprentissage. Toutefois, ces modèles sont limités par leur dépendance aux données annotées. En effet, pour être performants, ces modèles neuronaux ont besoin de corpus annotés de taille importante

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19

Karagol-Ayan, Burcu. "Resource generation from structured documents for low-density languages." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7580.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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20

Karim, Hiva. "Best way for collecting data for low-resourced languages." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35945.

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Анотація:

Low resource languages possess a limited number of digitized texts, making it challenging togenerate a satisfactory language audio corpus and information retrieval services. Low resourcelanguages, especially those spoken exclusively in African countries, lack a well-defined andannotated language corpus, making it a big obstacle for experts to provide a comprehensive textprocessing system. In this study, I Found out the best practices for producing and collectingdata for such zero/low resource languages by means of crowd-sourcing. For the purpose of thisstudy, a number of research articles (n=2

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21

Fapšo, Michal. "Vyhledávání výrazů v řeči pomocí mluvených příkladů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-261237.

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Tato práce se zabývá vyhledáváním výrazů v řeči pomocí mluvených příkladů (QbE STD). Výrazy jsou zadávány v mluvené podobě a jsou vyhledány v množině řečových nahrávek, výstupem vyhledávání je seznam detekcí s jejich skóre a časováním. V práci popisujeme, analyzujeme a srovnáváme tři různé přístupy ke QbE STD v jazykově závislých a jazykově nezávislých podmínkách, s jedním a pěti příklady na dotaz. Pro naše experimenty jsme použili česká, maďarská, anglická a arabská (levantská) data, a pro každý z těchto jazyků jsme natrénovali 3-stavový fonémový rozpoznávač. To nám dalo 16 možných kombinací

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22

Aufrant, Lauriane. "Training parsers for low-resourced languages : improving cross-lingual transfer with monolingual knowledge." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS089/document.

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Анотація:

Le récent essor des algorithmes d'apprentissage automatique a rendu les méthodes de Traitement Automatique des Langues d'autant plus sensibles à leur facteur le plus limitant : la qualité des systèmes repose entièrement sur la disponibilité de grandes quantités de données, ce qui n'est pourtant le cas que d'une minorité parmi les 7.000 langues existant au monde. La stratégie dite du transfert cross-lingue permet de contourner cette limitation : une langue peu dotée en ressources (la cible) peut être traitée en exploitant les ressources disponibles dans une autre langue (la source). Les progrès

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23

Mutuvi, Stephen. "Epidemic Event Extraction in Multilingual and Low-resource Settings." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS044.

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L'extraction d'événements épidémiques a pour but d'extraire de textes des incidents d'importance pour la santé publique, tels que des épidémies. Alors que l'extraction d'événements a fait l'objet de recherches approfondies pour les langues à fortes ressources comme l'anglais, les systèmes existants d'extraction d'événements épidémiques ne sont pas optimaux pour les contextes multilingues à faibles ressources en raison de la rareté des données d'entraînement. Tout d'abord, nous nous attaquons au problème de la rareté des données en transformant et en annotant un ensemble de données multilingues

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24

Vu, Ngoc Thang [Verfasser]. "Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information / Ngoc Thang Vu." Aachen : Shaker, 2014. http://d-nb.info/1058315811/34.

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25

Vu, Ngoc Thang [Verfasser], and T. [Akademischer Betreuer] Schultz. "Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information / Ngoc Thang Vu. Betreuer: T. Schultz." Karlsruhe : KIT-Bibliothek, 2014. http://d-nb.info/1051848229/34.

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26

Susman, Derya. "Turkish Large Vocabulary Continuous Speech Recognition By Using Limited Audio Corpus." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614207/index.pdf.

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Анотація:

Speech recognition in Turkish Language is a challenging problem in several perspectives. Most of the challenges are related to the morphological structure of the language. Since Turkish is an agglutinative language, it is possible to generate many words from a single stem by using suffixes. This characteristic of the language increases the out-of-vocabulary (OOV) words, which degrade the performance of a speech recognizer dramatically. Also, Turkish language allows words to be ordered in a free manner, which makes it difficult to generate robust language models. In this thesis, the existing m

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27

Cordova, Johanna. "Le quechua dans les outils numériques, un défi pour le TAL ? Développement de ressources linguistiques et numériques pour le quechua ancashino." Electronic Thesis or Diss., Paris, INALCO, 2024. http://www.theses.fr/2024INAL0031.

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Les langues quechuas constituent l'une des familles linguistiques amérindiennes comptant le plus grand nombre de locuteurs natifs. Au Pérou, selon le recensement de 2017, 13,9% de la population a le quechua pour première langue et environ 20% le parle. Pourtant, elle est presque totalement absente des usages numériques. En traitement automatique des langues (TAL), c'est une langue peu dotée, avec une forte disparité de ressources selon la variété de quechua considérée. L'objectif de cette thèse est de développer un ensemble d'outils fondamentaux pour le traitement automatique d'une variété du

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28

Cecovniuc, Ioana. "¿Qué prefiere usted, pagar en metálico o con cardo?: los falsos amigos y su concienciación lingüística en las aulas plurilingües de ELE." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/401588.

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Los falsos amigos constituyen una de las dificultades principales que afrontan los discentes que persiguen hablar, escribir e interaccionar en una lengua extranjera. Con un falso amigo se indica una palabra o estructura de un idioma extranjero que se asemeja, en lo escrito o en lo hablado, a una palabra o estructura en la lengua materna del hablante, pero que cuenta con un significado distinto. Abundan los ejemplos de falsas relaciones lingüísticas (totales o parciales) entre lengua materna y lengua meta. No obstante, la experiencia práctica respecto a este tema de investigación es bastante es

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29

Pronto, Lindon N. "Exploring German and American Modes of Pedagogical and Institutional Sustainability: Forging a Way into the Future." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/pitzer_theses/21.

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Rooted deep in Germany's past is its modern socio-political grounding for environmental respect and sustainability. This translates into individual and collective action and extends equally to the economic and policy realm as it does to educational institutions. This thesis evaluates research conducted in Germany with a view to what best approaches are transferable to the United States liberal arts setting. Furthermore, exemplary American models of institutional sustainability and environmental education are explored and combined with those from abroad to produce a blueprint and action plan fi

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30

Farra, Noura. "Cross-Lingual and Low-Resource Sentiment Analysis." Thesis, 2019. https://doi.org/10.7916/d8-x3b7-1r92.

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Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amount

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31

(8776265), Xiao Zhang. "Flexible Structured Prediction in Natural Language Processing with Partially Annotated Corpora." Thesis, 2020.

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Анотація:

<div>Structured prediction makes coherent decisions as structured objects to present the interrelations of these predicted variables. They have been widely used in many areas, such as bioinformatics, computer vision, speech recognition, and natural language processing. Machine Learning with reduced supervision aims to leverage the laborious and error-prone annotation effects and benefit the low-resource languages. In this dissertation we study structured prediction with reduced supervision for two sets of problems, sequence labeling and dependency parsing, both of which are representatives of

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32

Kamran, Amir. "Hybrid Machine Translation Approaches for Low-Resource Languages." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-313015.

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Анотація:

In recent years, corpus based machine translation systems produce significant results for a number of language pairs. However, for low-resource languages like Urdu the purely statistical or purely example based methods are not performing well. On the other hand, the rule-based approaches require a huge amount of time and resources for the development of rules, which makes it difficult in most scenarios. Hybrid machine translation systems might be one of the solutions to overcome these problems, where we can combine the best of different approaches to achieve quality translation. The goal of th

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33

Anoop, C. S. "Automatic speech recognition for low-resource Indian languages." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6195.

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Building good models for automatic speech recognition (ASR) requires large amounts of annotated speech data. Recent advancements in end-to-end speech recognition have aggravated the need for data. However, most Indian languages are low-resourced and lack enough training data to build robust and efficient ASR systems. Despite the challenges associated with the scarcity of data, Indian languages offer some unique characteristics that can be utilized to improve speech recognition in low-resource settings. Most languages have an overlapping phoneme set and a strong correspondence between their cha

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34

"Query-by-example spoken term detection for low-resource languages." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1290682.

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In this thesis, we consider the problem of query-by-example (QbyE) spoken term detection (STD) for low-resource languages. The problem is to automatically detect and locate the occurrences of a query term in a large audio database. The query term is given in the form of one or more audio examples. This research is motivated by the demand for information retrieval technologies that can handle speech data of low-resource languages. The major technical difficulty is that manual transcriptions and linguistic knowledge are not available for these languages.<br>The framework of acoustic segment mode

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35

Cooper, Erica Lindsay. "Text-to-Speech Synthesis Using Found Data for Low-Resource Languages." Thesis, 2019. https://doi.org/10.7916/d8-vdzp-j870.

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Text-to-speech synthesis is a key component of interactive, speech-based systems. Typically, building a high-quality voice requires collecting dozens of hours of speech from a single professional speaker in an anechoic chamber with a high-quality microphone. There are about 7,000 languages spoken in the world, and most do not enjoy the speech research attention historically paid to such languages as English, Spanish, Mandarin, and Japanese. Speakers of these so-called "low-resource languages" therefore do not equally benefit from these technological advances. While it takes a great deal of

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Tholpadi, Goutham. "Algorithms for Multilingual IR in Low Resource Languages using Weakly Aligned Corpora." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5360.

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Анотація:

Multilingual information retrieval (MLIR) methods generally rely on linguistic resources such as dictionaries, parallel corpora, etc., to overcome the language barrier. For low resource languages without these resources, an alternative approach is to use topical cross-lingual topical correspondence models learned from document-aligned multilingual corpora. However, there is a large and growing corpus of \weakly aligned" text in the form of user-generated content (UGC) data from social networks, commenting widgets, etc. that has been ignored by researchers so far. This task is made more c

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Lozano, Argüelles Cristina. "Formación y uso de la tecnología de los profesores de escuelas de inmersión en español." Thesis, 2014. http://hdl.handle.net/1805/6037.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>El propósito de esta investigación es ahondar en los usos tecnológicos de los profesores de español y en la formación que han recibido para integrar las TIC en sus clases. En concreto, nos interesa saber su actitud y nivel de seguridad ante la tecnología, de qué recursos disponen y cuáles utilizan en sus clases, cómo aprenden a utilizarlos (formal e informalmente), qué problemas perciben y cómo les gustaría mejorar la integración de la tecnología en sus clases. El estudio se centra en un grupo de escuelas de inmersión de español en

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Michell, Colin Simon. "Investigating the use of forensic stylistic and stylometric techniques in the analyses of authorship on a publicly accessible social networking site (Facebook)." Diss., 2013. http://hdl.handle.net/10500/13324.

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This research study examines the forensic application of a selection of stylistic and stylometric techniques in a simulated authorship attribution case involving texts on the social networking site, Facebook. Eight participants each submitted 2,000 words of self-authored text from their personal Facebook messages, and one of them submitted an extra 2,000 words to act as the ‘disputed text’. The texts were analysed in terms of the first 1,000 words received and then at the 2,000-word level to determine what effect text length has on the effectiveness of the chosen style markers (keywords, funct

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Mahabeer, Sandhya D. "Barriers in acquiring basic english reading and spelling skills by Zulu-speaking foundation phase learners." Diss., 2003. http://hdl.handle.net/10500/1166.

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This study focuses on the barriers that hinder the Zulu-speaking English second language learner in the Foundation Phase in acquiring basic reading and spelling skills. Nine hypotheses were developed from the literature study. Emanating from this, a quantitative empirical investigation, undertaken at various Foundation Phase schools in and around the greater Durban area, examined these barriers. A questionnaire was used as the main instrument in investigating these barriers. The study highlighted the relationships between the various variables. These relationships were, in the main, f

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