A MDL-based Model of Gender Knowledge Acquisition

By Harmony Marchal, Benoît Lemaire, Maryse Bianco, Philippe Dessus. 2008

3 citations recorded by the source corpus.

This paper presents an iterative model of knowledge acquisition of gender information associated with word endings in French. Gender knowledge is represented as a set of rules containing exceptions. Our model takes noun-gender pairs as input and constantly maintains a list of rules and exceptions which is both coherent with the input data and minimal with respect to a minimum description length criterion. This model was compared to human data at various ages and showed a good fit. We also compared the kind of rules discovered by the model with rules usually extracted by linguists and found interesting discrepancies.

Subfields: Artificial Intelligence

Citations in the corpus

• Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, unknown): “Badr, Zbib, and Glass (2008) N Y N N − − − − Marchal et al. (2008) N Y N N − − − − van Peursen (2009) N Y N N − − − − Badr, Zbib, and Glass (2009) N Y N N − − − − Garera and Yarowsky (2009) N Y Y Y N Y Y N Bergsma, Lin, and Goebel (2009) Y Y Y Y N Y Y N”