Predicting word order universals

Authors

  • Paola Merlo University of Geneva

Keywords:

frequency, word order universals, Greenberg’s Universal 20, computational modelling, probabilistic models, Naive Bayes

Abstract

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.

DOI:

https://doi.org/10.15398/jlm.v3i2.112

Full article

Published

2015-09-14

How to Cite

Merlo, P. (2015). Predicting word order universals. Journal of Language Modelling, 3(2), 317–344. https://doi.org/10.15398/jlm.v3i2.112

Issue

Section

Articles