Paper: Memory-Based Learning Of Morphology With Stochastic Transducers

ACL ID P02-1065
Title Memory-Based Learning Of Morphology With Stochastic Transducers
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

This paper discusses the supervised learn- ing of morphology using stochastic trans- ducers, trained using the Expectation- Maximization (EM) algorithm. Two ap- proaches are presented: first, using the transducers directly to model the process, and secondly using them to define a sim- ilarity measure, related to the Fisher ker- nel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learn- ing (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic.