Learning with Properties: Restrictiveness and Typological Structure

Natalie DelBusso


A learner's task is to find the most restrictive grammar consistent with the data of their language. This paper develops an OT learning algorithm that incorporates typological-level information from Property Analysis to increase restrictiveness and successfully learn subset languages. Based on Tesar's (2014) Output-Driven Learner (ODL), Property-ODL (PODL) uses ERCs taken from property values encoding specific markedness > faithfulness rankings. PODL was tested in a learning simulation for the phonological system in Tesar (2014), Paka, which presents the challenging case of languages in paradigmatic subset relations. In ODL, these require additional methods to be learned. PODL eliminates the need for these in learning the paradigmatic subsets and overall reduces the use of less-tested methods in learning the grammars of the typology.



Learnability; Property Theory; Output-Driven Learner

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DOI: https://doi.org/10.3765/amp.v8i0.4641

Copyright (c) 2020 Natalie DelBusso

License URL: https://creativecommons.org/licenses/by/3.0/