Software & Resources

MGL implementation (unavailable at the moment, but will be back soon)

Find below an R implementation of the Minimal Generalization Learner (MGL) model proposed by Adam Albright and Bruce Hayes.

Updates are regularly uploaded and documentation will be available in a not-too-distant future.

You are allowed to use and modify the program below, but if you do, please also cite Veríssimo and Clahsen (2014) in any publications or derivative code:

• Veríssimo, J. & Clahsen, H. (2014). Variables and similarity in linguistic generalization: Evidence from inflectional classes in Portuguese. Journal of Memory and Language, 76, 61–79. (MGL implementation, Version 0.9.10 [Computer software], retrieved from http://software.jverissimo.net/)

Several papers by Albright and Hayes provide detailed descriptions of the algorithm, as well as empirical tests of the MGL model:

• Albright, A. (2002). Islands of reliability for regular morphology: Evidence from Italian. Language, 78, 684-709.
• Albright, A. & Hayes, B. (2002). Modeling English past tense intuitions with minimal generalization. In M. Maxwell (Ed.), Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning (Vol. 6). Stroudsburg, PA: Association for Computational Linguistics.
• Albright, A. & Hayes, B. (2003). Rules vs. analogy in English past tenses: A computational/experimental study. Cognition, 90, 119-161.