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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Hodge, V.J., Austin, J. (2001). A Novel Binary Spell Checker. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_167
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DOI: https://doi.org/10.1007/3-540-44668-0_167
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