Готові списки джерел за темами / Low resource language

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Добірка наукової літератури з теми "Low resource language"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 29 липня 2025

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Статті в журналах з теми "Low resource language"

Pakray, Partha, Alexander Gelbukh, and Sivaji Bandyopadhyay. "Natural language processing applications for low-resource languages." Natural Language Processing 31, no. 2 (2025): 183–97. https://doi.org/10.1017/nlp.2024.33.

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AbstractNatural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training complex models. Low-resource languages, often spoken by smaller or marginalized communities, need help realizing the full potential of NLP applications. The primary challenges in developing NLP applications for low-resource languages stem from the need for large, well-annotated datasets, standardized tools, and linguistic resources. This

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Lin, Donghui, Yohei Murakami, and Toru Ishida. "Towards Language Service Creation and Customization for Low-Resource Languages." Information 11, no. 2 (2020): 67. http://dx.doi.org/10.3390/info11020067.

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The most challenging issue with low-resource languages is the difficulty of obtaining enough language resources. In this paper, we propose a language service framework for low-resource languages that enables the automatic creation and customization of new resources from existing ones. To achieve this goal, we first introduce a service-oriented language infrastructure, the Language Grid; it realizes new language services by supporting the sharing and combining of language resources. We then show the applicability of the Language Grid to low-resource languages. Furthermore, we describe how we ca

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Ranasinghe, Tharindu, and Marcos Zampieri. "Multilingual Offensive Language Identification for Low-resource Languages." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (2022): 1–13. http://dx.doi.org/10.1145/3457610.

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Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We

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Cassano, Federico, John Gouwar, Francesca Lucchetti, et al. "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs." Proceedings of the ACM on Programming Languages 8, OOPSLA2 (2024): 677–708. http://dx.doi.org/10.1145/3689735.

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Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks for research in programming languages and software engineering. However, the quality of code produced by a Code LLM varies significantly by programming language. Code LLMs produce impressive results on high-resource programming languages that are well represented in their training data (e.g., Java, Python, or JavaScript), but struggle with low-resource languages that have limited training data available (e.g., OCaml

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Abigail Rai. "Part-of-Speech (POS) Tagging of Low-Resource Language (Limbu) with Deep learning." Panamerican Mathematical Journal 35, no. 1s (2024): 149–57. http://dx.doi.org/10.52783/pmj.v35.i1s.2297.

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POS tagging is a basic Natural Language Processing (NLP) task that tags the words in an input text according to its grammatical values. Although POS Tagging is a fundamental application for very resourced languages, such as Limbu, is still unknown due to only few tagged datasets and linguistic resources. This research project uses deep learning techniques, transfer learning, and the BiLSTM-CRF model to develop an accurate POS-tagging system for the Limbu language. Using annotated and unannotated language data, we progress in achieving a small yet informative dataset of Limbu text. Skilled mult

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Nitu, Melania, and Mihai Dascalu. "Natural Language Processing Tools for Romanian – Going Beyond a Low-Resource Language." Interaction Design and Architecture(s), no. 60 (March 15, 2024): 7–26. http://dx.doi.org/10.55612/s-5002-060-001sp.

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Advances in Natural Language Processing bring innovative instruments to the educational field to improve the quality of the didactic process by addressing challenges like language barriers and creating personalized learning experiences. Most research in the domain is dedicated to high-resource languages, such as English, while languages with limited coverage, like Romanian, are still underrepresented in the field. Operating on low-resource languages is essential to ensure equitable access to educational opportunities and to preserve linguistic diversity. Through continuous investments in devel

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Zhou, Shuyan, Shruti Rijhwani, John Wieting, Jaime Carbonell, and Graham Neubig. "Improving Candidate Generation for Low-resource Cross-lingual Entity Linking." Transactions of the Association for Computational Linguistics 8 (July 2020): 109–24. http://dx.doi.org/10.1162/tacl_a_00303.

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Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for res

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Vargas, Francielle, Wolfgang Schmeisser-Nieto, Zohar Rabinovich, Thiago A. S. Pardo, and Fabrício Benevenuto. "Discourse annotation guideline for low-resource languages." Natural Language Processing 31, no. 2 (2025): 700–743. https://doi.org/10.1017/nlp.2024.19.

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AbstractMost existing discourse annotation guidelines have focused on the English language. As a result, there is a significant lack of research and resources concerning computational discourse-level language understanding and generation for other languages. To fill this relevant gap, we introduce the first discourse annotation guideline using the rhetorical structure theory (RST) for low-resource languages. Specifically, this guideline provides accurate examples of discourse coherence relations in three romance languages: Italian, Portuguese, and Spanish. We further discuss theoretical defini

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Li, Zihao, Yucheng Shi, Zirui Liu, et al. "Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28186–94. https://doi.org/10.1609/aaai.v39i27.35038.

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The development of Large Language Models (LLMs) relies on extensive text corpora, which are often unevenly distributed across languages. This imbalance results in LLMs performing significantly better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. Currently, there is a lack of quantitative methods to evaluate the performance of LLMs in these low-resource languages. To address this gap, we propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using inte

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Azragul Yusup, Azragul Yusup, Degang Chen Azragul Yusup, Yifei Ge Degang Chen, Hongliang Mao Yifei Ge, and Nujian Wang Hongliang Mao. "Resource Construction and Ensemble Learning based Sentiment Analysis for the Low-resource Language Uyghur." 網際網路技術學刊 24, no. 4 (2023): 1009–16. http://dx.doi.org/10.53106/160792642023072404018.

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To address the problem of scarce low-resource sentiment analysis corpus nowadays, this paper proposes a sentence-level sentiment analysis resource conversion method HTL based on the syntactic-semantic knowledge of the low-resource language Uyghur to convert high-resource corpus to low-resource corpus. In the conversion process, a k-fold cross-filtering method is proposed to reduce the distortion of data samples, which is used to select high-quality samples for conversion; finally, the Uyghur sentiment analysis dataset USD is constructed; the Baseline of this dataset is verified under

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Дисертації з теми "Low resource language"

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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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. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 131-142). 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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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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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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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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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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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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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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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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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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Книги з теми "Low resource language"

Chakravarthi, Bharathi Raja, Bharathi B, Miguel Ángel García Cumbreras, et al., eds. Speech and Language Technologies for Low-Resource Languages. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58495-4.

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M, Anand Kumar, Bharathi Raja Chakravarthi, Bharathi B, et al., eds. Speech and Language Technologies for Low-Resource Languages. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33231-9.

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Canadian Legal Information Centre. Plain Language Centre. Plain Language Resource Centre catalogue. Multiculturalism and Citizenship Canada], 1992.

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Cooper, Erica Lindsay. Text-to-Speech Synthesis Using Found Data for Low-Resource Languages. [publisher not identified], 2019.

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Bureau, Canada Translation, Promoting Access to Justice in Both Official Languages (Canada), Promotion de l'accès à la justice dans les deux langues officielles (Canada), and Canada. Bureau de la traduction., eds. Lexique du droit des fiducies (common law): Law of trusts glossary (common law) [electronic resource]. Bureau de la traduction = Translation Bureau, 2004.

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Library, Canada Multiculturalism and Citizenship Canada Departmental. Plain Language Resource Centre catalogue =: Catalogue du centre de ressources sur le langage clair et simple. Multiculturalism and Citizenship Canada], 1992.

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Megías, José Manuel Lucía. Literatura románica en Internet: Los textos. Editorial Castalia, 2002.

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Juda, Lawrence. International law and ocean use management. Routledge, 1996.

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Bundesanstalt für Geowissenschaften und Rohstoffe, ed. Glossary of shared water resources: Technical, socioeconomic and legal terminology : [English-Arabic]. United Nations, 2012.

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Частини книг з теми "Low resource language"

Palakodety, Shriphani, Ashiqur R. KhudaBukhsh, and Guha Jayachandran. "Language Identification." In Low Resource Social Media Text Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5625-5_4.

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Deshpande, Pranjali, and Sunita Jahirabadkar. "Low-Resource Language Document Summarization: A Challenge." In Data Science. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003283249-15.

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Grießhaber, Daniel, Ngoc Thang Vu, and Johannes Maucher. "Low-Resource Text Classification Using Domain-Adversarial Learning." In Statistical Language and Speech Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00810-9_12.

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Juan, Sarah Samson, Muhamad Fikri Che Ismail, Hamimah Ujir, and Irwandi Hipiny. "Language Modelling for a Low-Resource Language in Sarawak, Malaysia." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1289-6_14.

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Zhu, ShaoLin, Xiao Li, YaTing Yang, Lei Wang, and ChengGang Mi. "Learning Bilingual Lexicon for Low-Resource Language Pairs." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73618-1_66.

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Rakhimova, Diana, Eşref Adali, and Aidana Karibayeva. "Hybrid Approach Text Generation for Low-Resource Language." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70248-8_20.

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Ouily, Hamed Joseph, Aminata Sabané, Delwende Eliane Birba, Rodrique Kafando, Abdoul-Kader Kabore, and Tégawendé F. Bissyandé. "A Low-Resource Language Translation: French to Mooré." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88226-5_30.

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Charuka, Kaveesh, Sandareka Wickramanayake, Thanuja D. Ambegoda, Pasan Madhushan, and Dineth Wijesooriya. "Sign Language Recognition for Low Resource Languages Using Few Shot Learning." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8141-0_16.

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Chen, Yaqi, Hao Zhang, Xukui Yang, Wenlin Zhang, and Dan Qu. "Task-Consistent Meta Learning for Low-Resource Speech Recognition." In Natural Language Processing and Chinese Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44693-1_28.

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Baruah, Rupjyoti, and Anil Kumar Singh. "A Clinical Practice by Machine Translation on Low Resource Languages." In Natural Language Processing in Healthcare. CRC Press, 2022. http://dx.doi.org/10.1201/9781003138013-1.

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Тези доповідей конференцій з теми "Low resource language"

Zou, Shuai, Xuefeng Liang, and Yiyang Huang. "LipReading for Low-resource Languages by Language Dynamic LoRA." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889645.

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Zhang, Jiqiao, and Degen Huang. "Speech recognition for low-resource languages using large language models and related-language data." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070559.

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Downey, C. M., Terra Blevins, Dhwani Serai, Dwija Parikh, and Shane Steinert-Threlkeld. "Targeted Multilingual Adaptation for Low-resource Language Families." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.918.

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Sutherland, Emery M., Melvatha R. Chee, and Marios S. Pattichis. "Navajo Speech Recognition Using Low-Resource Language Models." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942828.

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Balachandran, Priyatharshan, Uthayasanker Thayasivam, Randil Pushpananda, and Ruvan Weerasinghe. "Towards Effective Emotion Analysis in Low-Resource Tamil Texts." In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.dravidianlangtech-1.101.

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Dash, Amulya, and Yashvardhan Sharma. "Towards Improving Translation Ability of Large Language Models on Low Resource Languages." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013319000003905.

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Nigatu, Hellina Hailu, Atnafu Lambebo Tonja, Benjamin Rosman, Thamar Solorio, and Monojit Choudhury. "The Zeno’s Paradox of ‘Low-Resource’ Languages." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.983.

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Kwok, Chin Yuen, Sheng Li, Jia Qi Yip, and Eng Siong Chng. "Low-resource Language Adaptation with Ensemble of PEFT Approaches." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848814.

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Sadat, Mobashir, and Cornelia Caragea. "Co-training for Low Resource Scientific Natural Language Inference." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.139.

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Lin, Jianbo, Yi Shen, Chuanyi Li, Changan Niu, and Bin Luo. "OptCodeTrans: Boost LLMs on Low-Resource Programming Language Translation." In 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge). IEEE, 2025. https://doi.org/10.1109/forge66646.2025.00014.

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Звіти організацій з теми "Low resource language"

Enimil, Sandra, Rachael Samberg, Erik Limpitlaw, Samantha Teremi, and Katie Zimmerman. e-Resource Licensing Explained: An A–Z Licensing Guidebook for Libraries. Association of Research Libraries, 2024. https://doi.org/10.29242/report.eresourcelicensing2024.

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ARL is pleased to publish e-Resource Licensing Explained: An A–Z Licensing Guidebook for Libraries, a practical tool to empower librarians who license electronic resources (e-resources). The guidebook includes easily digestible legal explanations and pragmatic strategies for preserving rights that users already have under US copyright law, particularly in the face of restrictive license terms that would otherwise constrain or eliminate those rights. For each term of an e-resource license agreement, the book explores: essentials of the law, desired results, desired language, tricks and traps, a

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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glos

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Marin, Anabel, and Gabriel Palazzo. Civic Power in Just Transitions: Blocking the Way or Transforming the Future? Institute of Development Studies, 2024. https://doi.org/10.19088/ids.2024.045.

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As the global shift towards a low-carbon economy accelerates, demand for critical minerals is projected to soar, intensifying pressures on supply chains and local environments. Policies like the European Union’s Critical Raw Materials Act and the U.S. Inflation Reduction Act are increasingly proposed to secure mineral access while upholding environmental standards and reducing ecological impacts. However, mining activities face significant civil resistance worldwide. This paper reveals that opposition to mineral extraction is a pervasive global phenomenon, spanning diverse sociopolitical conte

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Sitabkhan, Yasmin, Matthew C. H. Jukes, Eileen Dombrowski, and Indrah Munialo. Differentiated Instruction in Multigrade Preprimary Classrooms in Kenya. RTI Press, 2022. http://dx.doi.org/10.3768/rtipress.2022.op.0084.2212.

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There is little evidence of how differentiated instruction is being implemented, if at all, in low- and middle-income contexts, which often have unique challenges such as availability of resources and large class sizes. In this paper, we present the results of a qualitative study in eight multigrade preprimary classrooms in Kenya. We used classroom observations and teacher interviews to understand how teachers approached differentiation during language and mathematics lessons, including understanding why teachers were making the moves we observed. All teachers differentiated instruction to som

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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, et al. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.

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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of f

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Terzyan, Aram. The State of Minority Rights in Uzbekistan: A Comparative Analysis of Tajiks, Russians, and Koreans. Eurasia Institutes, 2023. http://dx.doi.org/10.47669/erd-1-2023.

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This paper examines the state of minority rights in Uzbekistan, focusing on three significant ethnic groups: Tajiks, Russians, and Koreans. It explores the historical context of these minorities, the cultural and linguistic challenges they face, socioeconomic issues, and their political representation. Under the authoritarian rule of Islam Karimov, Uzbekistan emphasized a unified Uzbek identity, often marginalizing minority cultures and languages. Despite President Shavkat Mirziyoyev’s reforms aimed at improving human rights, including the establishment of a Human Rights Ombudsman and the Deve

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Avellán, Leopoldo, and Steve Brito. Crossroads in a Fog: Navigating Latin America's Development Challenges with Text Analytics. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005489.

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Latin America and the Caribbean are facing challenging times due to a combination of worsening development gaps and limited fiscal space to address them. Furthermore, the region is contending with an unfavorable external environment. Issues such as rising poverty, climate change, inadequate infrastructure, and low-quality education and health services, among others, require immediate attention. Deciding how to prioritize efforts to address these development gaps is challenging due to their complexity and urgency, and setting priorities becomes even more difficult when resources are limited. Th

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F, Verdugo-Paiva, Izcovich A, Ragusa M, and Rada G. Lopinavir/ritonavir for the treatment of COVID-19: A living systematic review protocol. Epistemonikos Interactive Evidence Synthesis, 2024. http://dx.doi.org/10.30846/ies.4f3c02f030.

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Objective To assess the efficacy and safety of lopinavir/ritonavir for the treatment of patients with COVID-19. Design This is the protocol of a living systematic review. Data sources We will conduct searches in the [https://app.iloveevidence.com/loves/5e6fdb9669c00e4ac072701d](L.OVE platform for COVID-19), a system that maps PICO questions to a repository maintained through regular searches in electronic databases, preprint servers, trial registries and other resources relevant to COVID-19. No date or language restrictions will be applied. Eligibility criteria for selecting studies and method

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Rudman, Debbie Laliberte, and Rebecca M. Aldrich. Social Isolation, Third Places, and Precarious Employment Circumstances: A Scoping Review. University of Western Ontario, 2022. http://dx.doi.org/10.5206/otpub.2022.54.

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Rising rates of social isolation in Canada and other middle- and high-income countries have turned scholarly attention to the kinds of places that facilitate social connections. “Third places” - physical and virtual places beyond home (first places) and work (second places) - are thought to foster social interaction, connection, belonging, and support. This evidence brief reports on a SSHRC funded knowledge synthesis that linked understandings about “third places” with situations of precarious employment, given that people facing precarious employment circumstances often lack the social opport

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Does your Local Control Accountability (LCAP) Plan deliver on the promise of increased or improved services for English Learners? 10 research aligned rubrics to help answer the question and guide your program. The Center for Equity for English Learners (CEEL), 2015. http://dx.doi.org/10.15365/ceel.lcap2015.1.

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As California’s Local Control Funding Formula (LCFF) came into effect in 2013, districts were given more flexibility to use state resources and create a new school finance system to improve/increase services for students with greater needs for support, including English Learners (ELs), students from low-income backgrounds, and foster youth. Local Education Agencies (LEAs) were tasked with preparing the Local Control and Accountability Plans (LCAPs) to describe how districts use their plans to meet their annual goals for all students. To aid LEAs in their design and implementation of programs t

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