Typed Graph Models for Learning Latent Attributes from Names

Delip Rao and David Yarowsky
JHU


Abstract

This paper presents an original approach to semi-supervised learning of personal name ethnicity from typed graphs of morphophonemic features and first/last-name co-occurrence statistics. We frame this as a general solution to an inference problem over typed graphs where the edges represent labeled relations between features that are parameterized by the edge types. We propose a framework for parameter estimation on different constructions of typed graphs for this problem using a gradient-free optimization method based on grid search. Results on both in-domain and out-of-domain data show significant gains over 30\% accuracy improvement using the techniques presented in the paper.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-2090.pdf