TurboParser 2.0

Andre Martins — Thu, 11 Oct 2012 02:59:04 -0000

Dependency parsing is a lightweight syntactic formalism that relies on lexical relationships between words. Nonprojective dependency grammars may generate languages that are not context-free, offering a formalism that is arguably more adequate for some natural languages. Statistical parsers, learned from treebanks, have achieved the best performance in this task. While only local models (arc-factored) allow for exact inference, it has been shown that including non-local features and performing approximate inference can greatly increase performance.

This package contains a C++ implementation of a dependency parser based on the papers [1,2,3,4] below.

This package allows:

learning a parser/tagger from a treebank,
running a parser/tagger on new data,
evaluating the results against a gold-standard.

mloss.org TurboParser

TurboParser 2.0