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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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CorCenCC: Corpws Cenedlaethol Cymraeg Cyfoes – the National Corpus of Contemporary Welsh ...
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Participatory Research for Low-resourced Machine Translation:A Case Study in African Languages
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Infrastructure for Semantic Annotation in the Genomics Domain
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Open Welsh Language Resources for a Corpus Annotation Framework
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Leveraging Pre-Trained Embeddings for Welsh Taggers
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Abstract:
While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models. However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to re-invent the wheel. One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for part-of-speech and semantic tagging for Welsh using a pre-trained embedding model from FastText. Our model’s performance was compared with those of the existing rule-based stand-alone taggers for part-of-speech and semantic taggers. Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers.
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URL: https://eprints.lancs.ac.uk/id/eprint/135950/
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Corpus-Based Approaches to Igbo Diacritic Restoration
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Ezeani, Ignatius. - : University of Sheffield, 2019. : Computer Science (Sheffield), 2019
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