Boosting for Efficient Model Selection for Syntactic Parsing
We present an efficient model selection method using boosting for transition based constituency parsing. It is designed for exploring a high dimensional search space, defined by a large set of feature templates, as for example is typically the case when parsing morphologically rich languages. Our method removes the need to manually define heuristic constraints, which are often imposed in current state of the art selection methods. Our experiments for French show that the method is more efficient and is also capable of producing compact, state of the art models.
COLING