TY - GEN
T1 - Machine Learning Models of Universal Grammar Parameter Dependencies
AU - Kazakov, Dimitar Lubomirov
AU - Cordoni, Guido
AU - Ceolin, Andrea
AU - Irimia, Monica-Alexandrina
AU - Kim, Shin-Sook
AU - Michelioudakis, Dimitrios
AU - Radkevich, Nina
AU - Guardiano, Cristina
AU - Longobardi, Giuseppe
N1 - This is an author-produced version of the published paper. Uploaded with permission of the publisher/copyright holder. Further copying may not be permitted; contact the publisher for details
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
U2 - 10.26615/978-954-452-040-3_005
DO - 10.26615/978-954-452-040-3_005
M3 - Conference contribution
SN - 9789544520403
SP - 31
EP - 37
BT - Proceedings of The Knowledge Resources for the Socio-Economic Sciences and Humanities Workshop
ER -