Minimally Supervised Induction of Grammatical Gender
By Silviu Cucerzan, David Yarowsky. 2003
40 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Gender Studies
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Adina Williams
- Shane Bergsma
- Ryan Cotterell
- Randy Goebel
- Mohit Bansal
- Noah A. Smith
- Vlad Niculae
- Yonatan Belinkov
In Gender Studies
- Adina Williams
- Octavia Maria Sulea
- Vlad Niculae
- Liviu Petrisor Dinu
- Lawrence Wolf-Sonkin
- Shane Bergsma
- Dekang Lin
- Ryan Cotterell
Citations in the corpus
- Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender (2009, methodology): “While our approach is the first to exploit a dual or orthogonal representation of English noun gender, a bootstrapping approach has been applied to determining grammatical gender in other languages by Cucerzan and Yarowsky (2003).”
- Bootstrapping Without the Boot (2005, background): “Cucerzan and Yarowsky (2003) classified masculine vs. feminine nouns.”
- The Romanian Neuter Examined Through A Two-Gender N-Gram Classification System (2012, methodology): “approaches found in the works of (Cucerzan and Yarowsky, 2003) and (Nastase and Popescu, 2009) on automated classification of”
- What’s in a name? In some languages, grammatical gender (2009, background): “Cucerzan and Yarowsky (2003) present a bootstrapping process to predict gender for nouns in context.”
- Quantifying the Semantic Core of Gender Systems (2019, background): “In many languages, a noun’s grammatical gender can be predicted from its spelling and pronunciation (Cucerzan and Yarowsky, 2003; Nastase and Popescu, 2009).”