emnlp emnlp2013 emnlp2013-141 emnlp2013-141-reference knowledge-graph by maker-knowledge-mining
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Author: Hao Zhang ; Liang Huang ; Kai Zhao ; Ryan McDonald
Abstract: Online learning algorithms like the perceptron are widely used for structured prediction tasks. For sequential search problems, like left-to-right tagging and parsing, beam search has been successfully combined with perceptron variants that accommodate search errors (Collins and Roark, 2004; Huang et al., 2012). However, perceptron training with inexact search is less studied for bottom-up parsing and, more generally, inference over hypergraphs. In this paper, we generalize the violation-fixing perceptron of Huang et al. (2012) to hypergraphs and apply it to the cube-pruning parser of Zhang and McDonald (2012). This results in the highest reported scores on WSJ evaluation set (UAS 93.50% and LAS 92.41% respectively) without the aid of additional resources.