Efficient Stacked Dependency Parsing by Forest Reranking

Katsuhiko Hayashi, Shuhei Kondo, Yuji Matsumoto
2013 Transactions of the Association for Computational Linguistics   unpublished
This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discrim-inative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that
more » ... s the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.