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- W7606746 abstract "Accuracy of part-of-speech tagging is critical to downstream sub-tasks in front-end text analysis model of text-to-speech System. Uyghuris an agglutinative language in which numbers of words are formed by suffixes attaching to a stem (or root). Owing to there are unlimited new formed and derived syntactic words in Uyghur, Sizes of part-of-speech tagging set were big and out-of-vocabulary words often occurred in conventional Uyghur part-of-speech tagging method which directly trained and predicted the part-of-speech of word. To address this problem, this paper proposes the idea that trains the part-of-speech of stem and predicts the part-of-speech of word mainly by stem. Bi-gram language model is used to segment the stem and affix boundary of word, hidden markov model is used to train and predict part-of-speech of stem. In the end, rule adjusting method is used to adjust the changed part-of-speech of word when suffix attaching to a stem. Experimental result shows that proposed method obviously reduces the part-of-speech tagging error rate comparing to conventional part-of-speech tagging method." @default.
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- W7606746 date "2011-01-01" @default.
- W7606746 modified "2023-09-27" @default.
- W7606746 title "Morphological Analysis Based Part-of-Speech Tagging for Uyghur Speech Synthesis" @default.
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- W7606746 doi "https://doi.org/10.1007/978-3-642-25661-5_50" @default.
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