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.
|Title of host publication||Artificial neural networks : ICANN 2001 : International Conference, Vienna, Austria, August 21-25, 2001 : proceedings|
|Place of Publication||Berlin, Germany|
|Number of pages||5|
|Publication status||Published - 2001|
|Name||Lecture Notes in Computer Science|
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.