Learning Morphology with Pair Hidden Markov Models

Alexander Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper I present a novel Machine Learning approach to the acquisition of stochastic string transductions based on Pair Hidden Markov Models (PHMMs), a model used in computational biology. I show how these models can be used to learn morphological processes in a variety of languages, including English, German and Arabic. Previous techniques for learning morphology have been restricted to languages with essentially concatenative morphology.
Original languageEnglish
Title of host publicationProceedings of the Association for Computational Linguistics
Subtitle of host publicationStudent Session
Publication statusPublished - 1 Jul 2001

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