Maximum Likelihood Estimation of Feature-Based Distributions
Jeffrey Heinz, Cesar Koirala · 2010
Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention to what is minimally necessary to describe and learn phonological feature interactions in phonotactic patterns. The “bottom-up” approach adopted here is contrasted with the “top-down ” approach in Hayes and Wilson (2008), and it is argued that the bottom-up approach is more analytically transparent. 1