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- W2250553586 abstract "In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers." @default.
- W2250553586 created "2016-06-24" @default.
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- W2250553586 date "2014-01-01" @default.
- W2250553586 modified "2023-10-13" @default.
- W2250553586 title "Tagging The Web: Building A Robust Web Tagger with Neural Network" @default.
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- W2250553586 doi "https://doi.org/10.3115/v1/p14-1014" @default.
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