Results 1 to 10 of about 971,918 (170)
Building lexicon-based sentiment analysis model for low-resource languages [PDF]
MethodsX, 2023 Natural Language Processing (NLP) has transformed machine translation, sentiment analysis, information retrieval, and conversation systems. NLP applications rely on complete linguistic resources, which might be difficult for low-resource languages.
Idi Mohammed, Rajesh Prasad
doaj +2 more sources
Enhancing African low-resource languages: Swahili data for language modelling [PDF]
Data in Brief, 2020 Language modelling using neural networks requires adequate data to guarantee quality word representation which is important for natural language processing (NLP) tasks.
Casper S. Shikali, Refuoe Mokhosi
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Towards Language Service Creation and Customization for Low-Resource Languages
Information, 2020 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 ...
Donghui Lin, Yohei Murakami, Toru Ishida
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IEEE Access, 2020 This work applies a hierarchical transfer learning to implement deep neural network (DNN)-based multilingual text-to-speech (TTS) for low-resource languages. DNN-based system typically requires a large amount of training data.
Kurniawati Azizah +2 more
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Grammatical error correction for low-resource languages: a review of challenges, strategies, computational and future directions [PDF]
PeerJ Computer ScienceGrammatical error correction (GEC) is crucial for enhancing the readability and comprehension of texts, particularly in improving text quality in low-resource languages.
Syauqie Muhammad Marier +3 more
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IEEE AccessIn the field of computational linguistics, addressing machine translation (MT) challenges for low-resource languages remains crucial, as these languages often lack extensive data compared to high-resource languages.
Xiao Liang +4 more
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Low-Resource Languages Jailbreak GPT-4 [PDF]
arXiv.org, 2023 AI safety training and red-teaming of large language models (LLMs) are measures to mitigate the generation of unsafe content. Our work exposes the inherent cross-lingual vulnerability of these safety mechanisms, resulting from the linguistic inequality ...
Zheng-Xin Yong +2 more
semanticscholar +1 more source
Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages [PDF]
Frontiers in Artificial IntelligenceBhuvaneswari K, Varalakshmi M.
exaly +2 more sources
Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art [PDF]
Information Processing & Management, 2023 The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying a social problem taking place in virtual ...
Tanjim Mahmud +3 more
semanticscholar +1 more source
Multilingual Knowledge Graphs and Low-Resource Languages: A Review [PDF]
Transactions on Graph Data and Knowledge, 2023 There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine ...
Kaffee, Lucie-Aimée +4 more
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