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 contributionpeer-review

3 Citations (Scopus)


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, London, UK, August 27-29, 2009, Proceedings
EditorsDominic Palmer-Brown, Chrisina Draganova, Elias Pimenidis, Haris Mouratidis
Place of PublicationBerlin
PublisherSpringer Nature
Number of pages10
ISBN (Electronic)9783642039690
ISBN (Print)9783642039683
Publication statusPublished - 17 Aug 2009
Externally publishedYes

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


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


Dive into the research topics of 'Evolutionary ranking on multiple word correction algorithms using neural network approach'. Together they form a unique fingerprint.

Cite this