Learning with weighted transducers

Corinna Cortes; Mehryar Mohri

doi:10.3233/978-1-58603-975-2-14

Learning with weighted transducers

Corinna Cortes, Mehryar Mohri

Computer Science

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

Abstract

Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.

Original language	English (US)
Title of host publication	Finite-State Methods and Natural Language Processing
Publisher	IOS Press
Pages	14-22
Number of pages	9
Edition	1
ISBN (Print)	9781586039752
DOIs	https://doi.org/10.3233/978-1-58603-975-2-14
State	Published - 2009

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Number	1
Volume	191
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Keywords

Classification
Clustering
Kernels
Learning
Ranking
Rational powers series
Regression
Weighted automata
Weighted transducers

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3233/978-1-58603-975-2-14

Cite this

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title = "Learning with weighted transducers",

abstract = "Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.",

keywords = "Classification, Clustering, Kernels, Learning, Ranking, Rational powers series, Regression, Weighted automata, Weighted transducers",

author = "Corinna Cortes and Mehryar Mohri",

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AU - Cortes, Corinna

AU - Mohri, Mehryar

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AB - Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.

KW - Classification

KW - Clustering

KW - Kernels

KW - Learning

KW - Ranking

KW - Rational powers series

KW - Regression

KW - Weighted automata

KW - Weighted transducers

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M3 - Conference contribution

AN - SCOPUS:72749126565

SN - 9781586039752

T3 - Frontiers in Artificial Intelligence and Applications

SP - 14

EP - 22

BT - Finite-State Methods and Natural Language Processing

PB - IOS Press

ER -

Learning with weighted transducers

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Access to Document

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