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• W2969071806 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." @default.
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• W2969071806 date "2019-01-01" @default.
• W2969071806 modified "2023-09-26" @default.
• W2969071806 title "Leveraging Pre-Trained Embeddings for Welsh Taggers" @default.
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• W2969071806 doi "https://doi.org/10.18653/v1/w19-4332" @default.
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