Summary
In this paper the author presents a new similarity measure for strings of characters based on S3M which he expands to take into account not only the characters set and sequence but also their position.
After demonstrating the superiority of this new measure and discussing the need for a self adaptive spell checker, this work is further developed into an adaptive spell checker that produces a cluster with a defined number of words for each presented misspelled word. The accuracy of this solution is measured comparing its results against the results of the most widely used spell checker.
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References
Hodge, V.J., Austin, J.: A Comparison of a Novel Neural Spell Checker and Standard Spell Checking Algorithms. Pattern Recognition 35, 2571–2580 (2002)
Kukich, K.: Techniques for Automatically Correcting Words in Text. ACM Comput. Surveys 24(4), 377–439 (1992)
Dalianis, H.: Evaluating a Spelling Support in a Search Engine. In: Andersson, B., Bergholtz, M., Johannesson, P. (eds.) NLDB 2002. LNCS, vol. 2553, pp. 183–190. Springer, Heidelberg (2002)
Knutsson, O.: Automatisk språkgranskning av svensk text (in Swedish) (Automatic Proofreading of Swedish text). Licentiate Thesis, IPLAB-NADA, Royal Institute of Technology, KTH, Stockholm (2001)
Montgomery, D.J., Karlan, G.R., Coutinho: The Effectiveness of Word Processor Spell Checker Programs to Produce Target Words for Misspellings Generated by Students With Learning Disabilities. JSET E Journal 16(2) (2001)
Gerlach, G.J., Johnson, J.R., Ouyang, R.: Using an electronic speller to correct misspelled words and verify correctly spelled words. Reading Improvement 28, 188–194 (1991)
MacArthur, C.A., Graham, S., Haynes, J.B., DeLaPaz, S.: Spell checkers and students with learning disabilities: Performance comparisons and impact on spelling. Journal of Special Education 30, 35–57 (1996)
Garfinkel, R., Fernandez, E., Gobal, R.: Design of an Interactive spell checker: Optimizing the list of offered words. Decision Support Systems (35), 385–397 (2003)
Nuance, International CorrectSpell (2000), http://www.lhs.com/tech/icm/proofing/cs.asp (accessed 2003)
Seth, D., Kokar, M.M.: SSCS: A Smart Spell Checker System Implementation Using adaptive Software Architecture. In: Laddaga, R., Shrobe, H.E., Robertson, P. (eds.) IWSAS 2001. LNCS, vol. 2614, pp. 187–197. Springer, Heidelberg (2003)
Vaubel, K.P., Gettys, C.F.: Inferring user expertise for adaptive interfaces. Human Computer Interaction 5, 95–117 (1990)
Kumar, P., Bapi, R.S., Krishna, P.R.: SeqPAM: A Sequence Clustering Algorithm for Web Personalization. In: Poncelet, P., Masseglia, F., Teissseire, M. (eds.) Successes and New Directions in Data Mining, pp. 17–38. Information Science Reference, USA (2007)
Kumar, P., Rao, M.V., Krishna, P.R., Bapi, R.A., Laha, A.: Intrusion Detection System Using Sequence and Set Preserving Metric. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 498–504. Springer, Heidelberg (2005)
Mirkin, B.: Clustering for Data Mining: A Data Recovery Approach. Chapman and Hall/CRC, Boca Raton (2005)
Amorim, R.C.: Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge. In: Proceeding of Advanced Engineering Computing and Applications in Sciences, pp. 176–180. IEEE Computer Society, Los Alamitos (2008)
Aspell .Net, Spell Checker Test Kernel Results (2008) (updated May 14, 2008), http://aspell.net/test/cur/ (accessed February 10, 2009)
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Cordeiro de Amorim, R. (2009). An Adaptive Spell Checker Based on PS3M: Improving the Clusters of Replacement Words. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_61
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DOI: https://doi.org/10.1007/978-3-540-93905-4_61
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