A Markov Language Learning Model for Finite Parameter Spaces

This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explicitly calculated sample complexity learning times under different input distribution assumptions (including CHILDES database language input) and learning regimes. We also briefly describe a new way to formally model (rapid) diachronic syntax change. BACKGROUND MOTIVATION: TRIGGERS AND LANGUAGE ACQUISITION Recently, several researchers, including Gibson and Wexler (1994), henceforth GW, Dresher and Kaye (1990); and Clark and Roberts (1993) have modeled language learning in a (finite) space whose grammars are characterized by a finite number of parameters or nlength Boolean-valued vectors. Many current linguistic theories now employ such parametric models explicitly or in spirit, including Lexical-Functional Grammar and versions of HPSG, besides GB variants. With all such models, key questions about sample comple...

• Type: Conference
• Year: 1994
• Where: ACL
• Authors: Partha Niyogi, Robert C. Berwick
• Keywords: ACL 1994, ACL 2007, Complexity Learning Times, Language Learning, Sample Complexity