Memory-Based Learning Of Morphology With Stochastic Transducers


This paper discusses the supervised learning of morphology using stochastic transducers, trained using the Expectation-Maximization (EM) algorithm. Two approaches are presented: first, using the transducers directly to model the process, and secondly using them to define a similarity measure, related to the Fisher kernel method ( Jaakkola and Haussler, 1998 ), and then using a Memory-Based Learning(MBL) technique. These are evaluated and compared ondata sets from English, German, Slovene and Arabic.