A Best-First Probabilistic Shift-Reduce Parser

10 years 5 months ago
A Best-First Probabilistic Shift-Reduce Parser
Recently proposed deterministic classifierbased parsers (Nivre and Scholz, 2004; Sagae and Lavie, 2005; Yamada and Matsumoto, 2003) offer attractive alternatives to generative statistical parsers. Deterministic parsers are fast, efficient, and simple to implement, but generally less accurate than optimal (or nearly optimal) statistical parsers. We present a statistical shift-reduce parser that bridges the gap between deterministic and probabilistic parsers. The parsing model is essentially the same as one previously used for deterministic parsing, but the parser performs a best-first search instead of a greedy search. Using the standard sections of the WSJ corpus of the Penn Treebank for training and testing, our parser has 88.1% precision and 87.8% recall (using automatically assigned part-of-speech tags). Perhaps more interestingly, the parsing model is significantly different from the generative models used by other wellknown accurate parsers, allowing for a simple combination that...
Kenji Sagae, Alon Lavie
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Kenji Sagae, Alon Lavie
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