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• W2556232015 abstract "Transition-based parsing is useful for many NLP tasks. For improving the parsing accuracy, this paper proposes the following two enhancements based on a transition-based dependency parser with stack long short-term memory: using the context of a word in a sentence, and applying K-best decoding to expand the searching space. The experimental results show that the unlabeled and labeled attachment accuracies of our parser improve 0.70% and 0.87% over those of the baseline parser for English respectively, and are 0.82% and 0.86% higher than those of the baseline parser for Chinese respectively." @default.
• W2556232015 created "2016-11-30" @default.
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• W2556232015 date "2016-01-01" @default.
• W2556232015 modified "2023-09-23" @default.
• W2556232015 title "A Stack LSTM Transition-Based Dependency Parser with Context Enhancement and K-best Decoding" @default.
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• W2556232015 doi "https://doi.org/10.1007/978-3-319-49508-8_37" @default.
• W2556232015 hasPublicationYear "2016" @default.
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