MaltParser for .NET
MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank
data and to parse new data using an induced model. MaltParser
is developed by Johan Hall
, Jens Nilsson
and Joakim Nivre
at Växjö University and Uppsala University, Sweden.
MaltParser
implements nine deterministic parsing algorithms:
- Nivre arc-eager
- Nivre arc-standard
- Covington non-projective
- Covington projective
- Stack projective
- Stack swap-eager
- Stack swap-lazy
- Planar (implemented by
Carlos Gómez-Rodríguez
) - 2-planar (implemented by
Carlos Gómez-Rodríguez
)
MaltParser
allows users to define feature models of arbitrary complexity.
MaltParser
currently includes two machine learning packages (thanks to Sofia Cassel
for her work on LIBLINEAR):
LIBSVM
- A Library for Support Vector Machines (Chang, 2001).LIBLINEAR
-- A Library for Large Linear Classification (Fan et al., 2008).
MaltParser
can also be turned into a phrase structure parser that recovers both continuous and discontinuous phrases
with both phrase labels and grammatical functions (Hall and Nivre, 2008a; Hall and Nivre, 2008b).
PM> Install-Package MaltParser