Moreover, our parser outperforms previously reported shift-reduce constituency parsers while maintaining efficiency.
we make the following contributions in this paper: • We propose a set of novel features for better shift-reduce constituency parsing that is based on lexical dependencies obtained from large-scale auto-parsed data; • We empirically compare two different sources for obtaining lexical dependencies: humanlabeled treebank data and large-scale auto-parsed data respectively, and show the superiority of using auto-parsed data (Section 5.1); • We empirically analyze major sources of shift-reduce parsing errors (Section 3.1) and verify the effectiveness of new features in resolving shift-reduce action conflicts (Section 5.6.2); We use the beam-search shift-reduce parser (Zhang and Clark, 2009) as the baseline system in this paper.