Bibliografías temáticas / Less-resourced languages

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Literatura académica sobre el tema "Less-resourced languages"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 27 de julio de 2025

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Artículos de revistas sobre el tema "Less-resourced languages"

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Allah, Fadoua Ataa, and Siham Boulaknadel. "NEW TRENDS IN LESS-RESOURCED LANGUAGE PROCESSING: CASE OF AMAZIGH LANGUAGE." International Journal on Natural Language Computing 12, no. 2 (2023): 75–89. http://dx.doi.org/10.5121/ijnlc.2023.12207.

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The coronavirus (COVID-19) pandemic has dramatically changed lifestyles in much of the world. It forced people to profoundly review their relationships and interactions with digital technologies. Nevertheless, people prefer using these technologies in their favorite languages. Unfortunately, most languages are considered even as low or less-resourced, and they do not have the potential to keep up with the new needs. Therefore, this study explores how this kind of languages, mainly the Amazigh, will behave in the wholly digital environment, and what to expect for new trends. Contrary to last de
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Fadoua, Ataa Allah, and Boulaknadel Siham. "New Trends in Less-Resourced Language Processing: Case of Amazigh Language." International Journal on Natural Language Computing (IJNLC) 12, no. 2 (2023): 15. https://doi.org/10.5281/zenodo.8069560.

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The coronavirus (COVID-19) pandemic has dramatically changed lifestyles in much of the world. It forced people to profoundly review their relationships and interactions with digital technologies. Nevertheless, people prefer using these technologies in their favorite languages. Unfortunately, most languages are considered even as low or less-resourced, and they do not have the potential to keep up with the new needs. Therefore, this study explores how this kind of languages, mainly the Amazigh, will behave in the wholly digital environment, and what to expect for new trends. Contrary to last de
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3

Ulčar, Matej, Aleš Žagar, Carlos S. Armendariz, et al. "Mono- and cross-lingual evaluation of representation language models on less-resourced languages." Computer Speech & Language 95 (January 2026): 101852. https://doi.org/10.1016/j.csl.2025.101852.

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Dell’Orletta, Felice, Simonetta Montemagni, and Giulia Venturi. "Assessing document and sentence readability in less resourced languages and across textual genres." Recent Advances in Automatic Readability Assessment and Text Simplification 165, no. 2 (2014): 163–93. http://dx.doi.org/10.1075/itl.165.2.03del.

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In this paper, we tackle three underresearched issues of the automatic readability assessment literature, namely the evaluation of text readability in less resourced languages, with respect to sentences (as opposed to documents) as well as across textual genres. Different solutions to these issues have been tested by using and refining READ‑IT, the first advanced readability assessment tool for Italian, which combines traditional raw text features with lexical, morpho-syntactic and syntactic information. In READ‑IT readability assessment is carried out with respect to both documents and senten
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Nitu, Melania, and Mihai Dascalu. "Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian." Applied Sciences 14, no. 7 (2024): 2700. http://dx.doi.org/10.3390/app14072700.

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Authorship attribution for less-resourced languages like Romanian, characterized by the scarcity of large, annotated datasets and the limited number of available NLP tools, poses unique challenges. This study focuses on a hybrid Transformer combining handcrafted linguistic features, ranging from surface indices like word frequencies to syntax, semantics, and discourse markers, with contextualized embeddings from a Romanian BERT encoder. The methodology involves extracting contextualized representations from a pre-trained Romanian BERT model and concatenating them with linguistic features, sele
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Neshir, Girma, Andreas Rauber, and Solomon Atnafu. "Meta-Learner for Amharic Sentiment Classification." Applied Sciences 11, no. 18 (2021): 8489. http://dx.doi.org/10.3390/app11188489.

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The emergence of the World Wide Web facilitates the growth of user-generated texts in less-resourced languages. Sentiment analysis of these texts may serve as a key performance indicator of the quality of services delivered by companies and government institutions. The presence of user-generated texts is an opportunity for assisting managers and policy-makers. These texts are used to improve performance and increase the level of customers’ satisfaction. Because of this potential, sentiment analysis has been widely researched in the past few years. A plethora of approaches and tools have been d
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Pelicon, Andraž, Ravi Shekhar, Blaž Škrlj, Matthew Purver, and Senja Pollak. "Investigating cross-lingual training for offensive language detection." PeerJ Computer Science 7 (June 25, 2021): e559. http://dx.doi.org/10.7717/peerj-cs.559.

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Platforms that feature user-generated content (social media, online forums, newspaper comment sections etc.) have to detect and filter offensive speech within large, fast-changing datasets. While many automatic methods have been proposed and achieve good accuracies, most of these focus on the English language, and are hard to apply directly to languages in which few labeled datasets exist. Recent work has therefore investigated the use of cross-lingual transfer learning to solve this problem, training a model in a well-resourced language and transferring to a less-resourced target language; bu
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Akhtar, Md Shad, Palaash Sawant, Sukanta Sen, Asif Ekbal, and Pushpak Bhattacharyya. "Improving Word Embedding Coverage in Less-Resourced Languages Through Multi-Linguality and Cross-Linguality." ACM Transactions on Asian and Low-Resource Language Information Processing 18, no. 2 (2019): 1–22. http://dx.doi.org/10.1145/3273931.

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Robnik-Šikonja, Marko, Kristjan Reba, and Igor Mozetič. "Cross-lingual transfer of sentiment classifiers." Slovenščina 2.0: empirical, applied and interdisciplinary research 9, no. 1 (2021): 1–25. http://dx.doi.org/10.4312/slo2.0.2021.1.1-25.

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Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embeddings transform vector spaces of different languages so that similar words are aligned. This is done by mapping one language’s vector space to the vector space of another language or by construction of a joint vector space for multiple languages. Cross-lingual embeddings can be used to transfer machine learning models between languages, thereby compensating for insufficient data in less-resourced languages. We use c
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Hutin, Mathilde, and Marc Allassonnière-Tang. "Operation LiLi: Using Crowd-Sourced Data and Automatic Alignment to Investigate the Phonetics and Phonology of Less-Resourced Languages." Languages 7, no. 3 (2022): 234. http://dx.doi.org/10.3390/languages7030234.

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Less-resourced languages are usually left out of phonetic studies based on large corpora. We contribute to the recent efforts to fill this gap by assessing how to use open-access, crowd-sourced audio data from Lingua Libre for phonetic research. Lingua Libre is a participative linguistic library developed by Wikimedia France in 2015. It contains more than 670k recordings in approximately 150 languages across nearly 740 speakers. As a proof of concept, we consider the Inventory Size Hypothesis, which predicts that, in a given system, variation in the realization of each vowel will be inversely
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Tesis sobre el tema "Less-resourced languages"

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Millour, Alice. "Myriadisation de ressources linguistiques pour le traitement automatique de langues non standardisées." Thesis, Sorbonne université, 2020. http://www.theses.fr/2020SORUL126.

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Les sciences participatives, et en particulier la myriadisation (crowdsourcing) bénévole, représentent un moyen peu exploité de créer des ressources langagières pour certaines langues encore peu dotées, et ce malgré la présence de locuteurs sur le Web. Nous présentons dans ce travail les expériences que nous avons menées pour permettre la myriadisation de ressources langagières dans le cadre du développement d'un outil d'annotation automatique en parties du discours. Nous avons appliqué cette méthodologie à trois langues non standardisées, en l'occurrence l'alsacien, le créole guadeloupéen et
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Capítulos de libros sobre el tema "Less-resourced languages"

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Saralegi, Xabier, Iñaki San Vicente, and Irati Ugarteburu. "Cross-Lingual Projections vs. Corpora Extracted Subjectivity Lexicons for Less-Resourced Languages." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37256-8_9.

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Leite, Bernardo, Tomás Freitas Osório, and Henrique Lopes Cardoso. "FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72315-5_16.

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Shih, Meng-hsien. "The Application of Chinese Word Segmentation to Less-Resourced Language Processing." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0586-3_14.

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Vetulani, Zygmunt, and Grażyna Vetulani. "Development of Real Size IT Systems with Language Competence as a Challenge for a Less-Resourced Language." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_59.

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Maritxalar, Montse, Elaine Uí Donnchadha, Jennifer Foster, and Monica Ward. "Quizzes on Tap: Exporting a Test Generation System from One Less-Resourced Language to Another." In Human Language Technology Challenges for Computer Science and Linguistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14120-6_41.

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Gruzitis, Normunds, Roberts Dargis, Viesturs Julijs Lasmanis, Ginta Garkaje, and Didzis Gosko. "Adapting Automatic Speech Recognition to the Radiology Domain for a Less-Resourced Language: The Case of Latvian." In Intelligent Sustainable Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6309-3_27.

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Ataa, Fadoua, and Siham Boulaknadel. "Toward Computational Processing of Less Resourced Languages: Primarily Experiments for Moroccan Amazigh Language." In Theory and Applications for Advanced Text Mining. InTech, 2012. http://dx.doi.org/10.5772/51069.

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Laakso, Johanna. "The making of the Uralic nation-state languages." In The Oxford Guide to the Uralic Languages. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198767664.003.0003.

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Among the Uralic languages, most of which are endangered, under-resourced, used largely in spoken in-group communication, and less well researched, Hungarian, Finnish, and Estonian stand out as well-documented nation-state languages with a strong literary tradition and use in all walks of life, including the domains of education, culture, business, and administration. As the only non-Indo-European state languages in continental Europe, these three languages have gone through similar developments: an ethnolinguistic awakening and consciousness-raising propelled by Romantic Nationalism, and an i
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Ward, Monica. "Joining the blocks together – an NLP pipeline for CALL development." In CALL and complexity – short papers from EUROCALL 2019. Research-publishing.net, 2019. http://dx.doi.org/10.14705/rpnet.2019.38.1043.

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Intelligent Computer-Assisted Language Learning (ICALL) involves using tools and techniques from computational linguistics and Natural Language Processing (NLP) in the language learning process. It is an inherently complex endeavour and is multi-, inter-, and trans-disciplinary in nature. Often these tools and techniques are designed for tasks and purposes other than language learning, and this makes their adaptation and use in the CALL domain difficult. It can be even more challenging for Less-Resourced Languages (LRLs) for CALL researchers to adapt or incorporate NLP into CALL artefacts. Thi
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Skadiņa, Inguna, and Didzis Goško. "Towards Hybrid Model for Human-Computer Interaction in Latvian." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200609.

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Human-computer interaction, especially in form of dialogue systems and chatbots, has become extremely popular during the last decade. The dominant approach in the recent development of practical virtual assistants is the application of deep learning techniques. However, in case of less resourced language (or domain), the application of deep learning could be very complicated due to the lack of necessary training data. In this paper, we discuss possibility to apply hybrid approach to dialogue modelling by combining data-driven approach with the knowledge-based approach. Our hypothesis is that b
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Actas de conferencias sobre el tema "Less-resourced languages"

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Palen-Michel, Chester, and Constantine Lignos. "LR-Sum: Summarization for Less-Resourced Languages." In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.427.

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Huyssteen, Gerhard B. van, and Martin J. Puttkammer. "Accelerating the annotation of lexical data for less-resourced languages." In Interspeech 2007. ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-435.

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Millour, Alice, and Karën Fort. "Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling." In Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_090.

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Draxler, Christoph. "On web-based creation of speech resources for less-resourced languages." In Interspeech 2007. ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-436.

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Tachbelie, Martha Yifiru, Solomon Teferra Abate, and Tanja Schultz. "Development of Multilingual ASR Using GlobalPhone for Less-Resourced Languages: The Case of Ethiopian Languages." In Interspeech 2020. ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2827.

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Urbizu, Gorka, Ander Soraluze, and Olatz Arregi. "Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque." In Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-2806.

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Abate, Solomon Teferra, Martha Yifiru Tachbelie, and Tanja Schultz. "End-to-End Multilingual Automatic Speech Recognition for Less-Resourced Languages: The Case of Four Ethiopian Languages." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9415020.

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Bedi, Branislav, Jane O’Toole, and Monica Ward. "Resourceful approaches in CALL for less-commonly taught languages (LCTLs): Case studies on Icelandic, Irish, and Nawat." In EuroCALL 2023: CALL for all Languages. Editorial Universitat Politécnica de Valéncia, 2023. http://dx.doi.org/10.4995/eurocall2023.2023.16995.

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This article explores challenges in supporting Less-Commonly Taught Languages (LCTLs) both in and outside of traditional classroom settings. The focus here is on three case studies of Icelandic as a second and foreign language (L2), Irish as a second or additional language (L2/ IAL), and Nawat as an endangered language. The linguistic contexts of the featured languages may differ- but all three have common ground in being less digitally resourced. Common practices in Computer-Assisted Language Learning (CALL) have shown that limited access to digital resources not only hampers development of C
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Hassani, Hossein, and Oussama H. Hamid. "Using Artificial Neural Networks in Dialect Identification in Less-resourced Languages - The Case of Kurdish Dialects Identification." In Soft Computing Techniques: A Nature-Inspired Framework for more Advanced Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006578004430448.

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Mihajlik, Péter, Máté Soma Kádár, Gergely Dobsinszki, et al. "What Kind of Multi- or Cross-lingual Pre-training is the most Effective for a Spontaneous, Less-resourced ASR Task?" In 2nd Annual Meeting of the ELRA/ISCA SIG on Under-resourced Languages (SIGUL 2023). ISCA, 2023. http://dx.doi.org/10.21437/sigul.2023-13.

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