Minimally supervised induction of grammatical gender

Authors:
Silviu Cucerzan;David Yarowsky
Affiliations:
Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD
Venue:
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Year:
2003

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Abstract

This paper investigates the problem of determining grammatical gender for the nouns of a language starting with minimal resources: a very small list of seed nouns for which gender is known or via translingual projection of natural gender. We show that through a bootstrapping process that uses contextual clues from an unannotated corpus and morphological clues modeled with suffix tries, accurate gender predictions can be induced for five diverse test languages.