Memory-Based Learning of Morphology with Stochastic Transducers

Alexander Clark

Memory-Based Learning of Morphology with Stochastic Transducers

Alexander Clark

Philosophy

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

20 Citations (Scopus)

Abstract

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, and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic.

Original language	Undefined/Unknown
Title of host publication	Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL)
Pages	513-520
Number of pages	8
Publication status	Published - 2002

Cite this

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abstract = "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, and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic.",

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