Paper: A MDL-based Model of Gender Knowledge Acquisition

ACL ID W08-2110
Title A MDL-based Model of Gender Knowledge Acquisition
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008

This paper presents an iterative model of knowledge acquisition of gender infor- mation associated with word endings in French. Gender knowledge is represented as a set of rules containing exceptions. Our model takes noun-gender pairs as in- put and constantly maintains a list of rules and exceptions which is both coher- ent 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.