Predicting word order universals
Keywords:frequency, word order universals, Greenberg’s Universal 20, computational modelling, probabilistic models, Naive Bayes
This paper shows a computational learning paradigm to compare and
test theories about language universals. Its main contribution lies in
the illustration of the encoding and comparison of theories about typological
universals to measure the generalisation ability of these theories.
In so doing, this method uncovers hidden dependencies between
theoretical dimensions and primitives that were considered independent
and independently motivated.
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Copyright (c) 2015 Paola Merlo
This work is licensed under a Creative Commons Attribution 3.0 Unported License.