Memory-Based Learning of Morphology with Stochastic Transducers. / Clark, Alexander.

Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL). 2002. p. 513--520.

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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.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL)
Pages513--520
Publication statusPublished - 2002

ID: 1318671