Learning Morphology with Pair Hidden Markov Models

Alexander Clark

Learning Morphology with Pair Hidden Markov Models

Alexander Clark

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

Abstract

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 language	English
Title of host publication	Proceedings of the Association for Computational Linguistics
Subtitle of host publication	Student Session
Pages	55-60
Publication status	Published - 1 Jul 2001

Cite this

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title = "Learning Morphology with Pair Hidden Markov Models",

abstract = "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.",

author = "Alexander Clark",

year = "2001",

month = jul,

day = "1",

language = "English",

pages = "55--60",

booktitle = "Proceedings of the Association for Computational Linguistics",

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AB - 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.

M3 - Conference contribution

SP - 55

EP - 60

BT - Proceedings of the Association for Computational Linguistics

ER -