Partially Supervised Learning of Morphology with Stochastic Transducers
Alexander Clark · 2001
In this paper I present an algorithm for the unsupervised learning of morphology using stochastic finite state transducers, in particular Pair Hidden Markov Models. The task is viewed as an alignment problem between two sets of words. A supervised model of morphology acquisition is converted to an unsupervised model by treating the alignment as a further hidden variable. The use of the Expectation-Maximisation algorithm for this task is studied, which leads to calculations involving the permanent of a matrix of probabilities.