Minimally Supervised Induction of Grammatical Gender

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.