Paper: Typed Graph Models for Learning Latent Attributes from Names

ACL ID P11-2090
Title Typed Graph Models for Learning Latent Attributes from Names
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

This paper presents an original approach to semi-supervised learning of personal name ethnicity from typed graphs of morphophone- mic features and first/last-name co-occurrence statistics. We frame this as a general solu- tion to an inference problem over typed graphs where the edges represent labeled relations be- tween features that are parameterized by the edge types. We propose a framework for parameter estimation on different construc- tions of typed graphs for this problem us- ing 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.