White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

A comparison of standard spell checking algorithms and a novel binary neural approach

Hodge, V.J. and Austin, J. (2003) A comparison of standard spell checking algorithms and a novel binary neural approach. IEEE Transactions on Knowledge and Data Engineering. pp. 1073-1081.

Full text available as:
[img]
Preview
Text (hodgevj2.pdf)
hodgevj2.pdf

Download (782Kb)

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 integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. In this paper, we evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost.

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: binary neural spell checker, integrated modular spell checker, associative matching, ERRORS, WORDS
Academic Units: The University of York > Computer Science (York)
Depositing User: Sherpa Assistant
Date Deposited: 30 Sep 2005
Last Modified: 17 Oct 2013 14:29
Published Version: http://dx.doi.org/10.1109/TKDE.2003.1232265
Status: Published
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/689

Actions (login required)

View Item View Item