@InProceedings{kazakov-EtAl:2017:KnowRSH,
  author    = {Kazakov, Dimitar  and  Cordoni, Guido  and  Ceolin, Andrea  and  Irimia, Monica-Alexandrina  and  Kim, Shin-Sook  and  Michelioudakis, Dimitris  and  Radkevich, Nina  and  Guardiano, Cristina  and  Longobardi, Giuseppe},
  title     = {Machine Learning Models of Universal Grammar Parameter Dependencies},
  booktitle = {Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Inc.},
  pages     = {31--37},
  abstract  = {The use of parameters in the description of natural language syntax has to
	balance between the need to discriminate among (sometimes subtly different)
	languages, which can be seen as a cross-linguistic version of Chomsky's (1964)
	descriptive adequacy, and the complexity of the acquisition task that a large
	number of parameters would imply, which is a problem for explanatory adequacy.
	Here we present a novel approach in which a machine learning algorithm is used
	to find dependencies in a table of parameters. The result is a dependency graph
	in which some of the parameters can be fully predicted from others. These
	empirical findings can be then subjected to linguistic analysis, which may
	either refute them by providing typological counter-examples of languages not
	included in the original dataset, dismiss them on theoretical grounds, or
	uphold them as tentative empirical laws worth of further study.},
  url       = {https://doi.org/10.26615/978-954-452-040-3_005}
}