@InProceedings{bawden-crabbe:2016:COLING,
  author    = {Bawden, Rachel  and  Crabb\'{e}, Beno\^{i}t},
  title     = {Boosting for Efficient Model Selection for Syntactic Parsing},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1--11},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/C16-1001}
}