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• W2124861326 abstract "Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedy and only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging equivalent stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster." @default.
• W2124861326 created "2016-06-24" @default.
• W2124861326 creator A5006404928 @default.
• W2124861326 creator A5070742827 @default.
• W2124861326 date "2010-07-11" @default.
• W2124861326 modified "2023-09-22" @default.
• W2124861326 title "Dynamic Programming for Linear-Time Incremental Parsing" @default.
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