@InProceedings{jimenezlopez-becerrabonache:2016:CL4LC,
  author    = {Jimenez Lopez, Maria Dolores  and  Becerra-Bonache, Leonor},
  title     = {Could Machine Learning Shed Light on Natural Language Complexity?},
  booktitle = {Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1--11},
  abstract  = {In this paper, we propose to use a subfield of machine learning --grammatical
	inference-- to measure linguistic complexity from a developmental point of
	view. We focus on relative complexity by considering a child learner in the
	process of first language acquisition. The relevance of grammatical inference
	models for measuring linguistic complexity from a developmental point of view
	is based on the fact that algorithms proposed in this area can be considered
	computational models for studying first language acquisition. Even though it
	will be possible to use different techniques from the field of machine learning
	as computational models for dealing with linguistic complexity --since in any
	model we have algorithms that can learn from data--, we claim that grammatical
	inference models offer some advantages over other tools.},
  url       = {http://aclweb.org/anthology/W16-4101}
}