Abstract
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 discriminative 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 eliminates 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.- Anthology ID:
- Q13-1012
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 139–150
- Language:
- URL:
- https://aclanthology.org/Q13-1012
- DOI:
- 10.1162/tacl_a_00216
- Cite (ACL):
- Katsuhiko Hayashi, Shuhei Kondo, and Yuji Matsumoto. 2013. Efficient Stacked Dependency Parsing by Forest Reranking. Transactions of the Association for Computational Linguistics, 1:139–150.
- Cite (Informal):
- Efficient Stacked Dependency Parsing by Forest Reranking (Hayashi et al., TACL 2013)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/Q13-1012.pdf