Feature Forest Models for Syntactic Parsing
Yusuke Miyao
While existing models for probabilistic parsing decompose the
probability of parsing results into that of primitive dependencies of
two words, our model selects the most probable parsing result from a
set of candidates allowed by a lexicalized grammar. Since parsing
results given by the lexicalized grammar cannot be decomposed into
independent sub-events, we apply a feature forest model, which allows
probabilistic modeling of the complete parse results without the
decomposition into independent sub-events. Our approach provides a
general method of producing a consistent probabilistic model of
parsing results given by lexicalized grammars.