A novel binary spell checker

V.J. Hodge; J. Austin; G. Dorffner; H. Bischof; K. Hornik

A novel binary spell checker

V.J. Hodge, J. Austin, G. Dorffner (Editor), H. Bischof (Editor), K. Hornik (Editor)

Computer Science

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

Abstract

In this paper we propose a simple, flexible and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning and associative matching in the AURA neural system. We evaluate our approach against several benchmark spell-checking algorithms for recall accuracy. Our proposed hybrid methodology has the joint highest top 10 recall rate of the techniques evaluated. The method has a high recall rate and low computational cost.

Original language	English
Title of host publication	Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	1199-1204
Number of pages	5
ISBN (Print)	3540424865
Publication status	Published - 2001

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer-Verlag
Volume	2130

Bibliographical note

Copyright © 2001 Springer-Verlag. This is an author produced version of a chapter published in Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Access to Document

hodgevj7.pdf

Cite this

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title = "A novel binary spell checker",

abstract = "In this paper we propose a simple, flexible and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning and associative matching in the AURA neural system. We evaluate our approach against several benchmark spell-checking algorithms for recall accuracy. Our proposed hybrid methodology has the joint highest top 10 recall rate of the techniques evaluated. The method has a high recall rate and low computational cost.",

author = "V.J. Hodge and J. Austin and G. Dorffner and H. Bischof and K. Hornik",

note = "Copyright {\textcopyright} 2001 Springer-Verlag. This is an author produced version of a chapter published in Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.",

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A novel binary spell checker. / Hodge, V.J.; Austin, J.; Dorffner, G. (Editor) et al.
Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings. Berlin, Germany: Springer, 2001. p. 1199-1204 (Lecture Notes in Computer Science; Vol. 2130).

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

TY - CHAP

T1 - A novel binary spell checker

AU - Hodge, V.J.

AU - Austin, J.

A2 - Dorffner, G.

A2 - Bischof, H.

A2 - Hornik, K.

N1 - Copyright © 2001 Springer-Verlag. This is an author produced version of a chapter published in Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

PY - 2001

Y1 - 2001

N2 - In this paper we propose a simple, flexible and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning and associative matching in the AURA neural system. We evaluate our approach against several benchmark spell-checking algorithms for recall accuracy. Our proposed hybrid methodology has the joint highest top 10 recall rate of the techniques evaluated. The method has a high recall rate and low computational cost.

AB - In this paper we propose a simple, flexible and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning and associative matching in the AURA neural system. We evaluate our approach against several benchmark spell-checking algorithms for recall accuracy. Our proposed hybrid methodology has the joint highest top 10 recall rate of the techniques evaluated. The method has a high recall rate and low computational cost.

M3 - Chapter

SN - 3540424865

T3 - Lecture Notes in Computer Science

SP - 1199

EP - 1204

BT - Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings

PB - Springer

CY - Berlin, Germany

ER -