Evolutionary ranking on multiple word correction algorithms using neural network approach

Jun Li, Karim Ouazzane, Yanguo Jing, Hassan Kazemian, Richard Boyd

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

2 Citations (Scopus)

Abstract

Multiple algorithms have been developed to correct user's typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks
Subtitle of host publication11th International Conference, EANN 2009, Proceedings
EditorsDominic Palmer-Brown, Chrisina Draganova, Elias Pimenidis, Haris Mouratidis
PublisherSpringer Verlag
Pages409-418
Number of pages10
ISBN (Print)3642039685, 9783642039683
DOIs
Publication statusPublished - 1 Dec 2009
Event11th International Conference on Engineering Applications of Neural Networks, EANN 2009 - London, United Kingdom
Duration: 27 Aug 200929 Aug 2009

Publication series

NameCommunications in Computer and Information Science
Volume43 CCIS
ISSN (Print)1865-0929

Conference

Conference11th International Conference on Engineering Applications of Neural Networks, EANN 2009
CountryUnited Kingdom
CityLondon
Period27/08/0929/08/09

Keywords

  • Jaro distance
  • Jaro-Winkler distance
  • Levenshtein distance
  • Metaphone
  • Neural Network
  • ranking First Hitting Rate
  • word 2-gram

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Li, J., Ouazzane, K., Jing, Y., Kazemian, H., & Boyd, R. (2009). Evolutionary ranking on multiple word correction algorithms using neural network approach. In D. Palmer-Brown, C. Draganova, E. Pimenidis, & H. Mouratidis (Eds.), Engineering Applications of Neural Networks: 11th International Conference, EANN 2009, Proceedings (pp. 409-418). (Communications in Computer and Information Science; Vol. 43 CCIS). Springer Verlag. https://doi.org/10.1007/978-3-642-03969-0_38