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• W2952054097 abstract "We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging." @default.
• W2952054097 created "2019-06-27" @default.
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• W2952054097 date "2017-04-05" @default.
• W2952054097 modified "2023-09-27" @default.
• W2952054097 title "Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF" @default.
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• W2952054097 doi "https://doi.org/10.48550/arxiv.1704.01314" @default.
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