A Bayesian Model for Discovering Typological Implications
Computation and Language
2009-07-07 v1
Abstract
A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.'' Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent.
Cite
@article{arxiv.0907.0785,
title = {A Bayesian Model for Discovering Typological Implications},
author = {Hal Daumé and Lyle Campbell},
journal= {arXiv preprint arXiv:0907.0785},
year = {2009}
}