A neural network based solution for automatic typing errors correction

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

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Multiple algorithms have been developed to correct user's typing mistakes. However, an optimal 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 motivated this research to synthesize 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 with key factors such as Time Change, Context Change and User Feedback being considered. Experimental results show that the developed WLR model (i. e., word-list neural network ranking model) achieves the best results in comparison with Levenshtein distance, Metaphone. and Two-Gram prediction algorithms throughout all stages. It achieves 57.50% Ranking First Hitting Rate with samples of category one and a best Ranking First Hitting Rate of 74.69% within category four. Further work is recommended to extend the number of parameters and integrate more algorithms to achieve a higher level of accuracy.

Original languageEnglish
Pages (from-to)889-896
Number of pages8
JournalNeural Computing and Applications
Volume20
Issue number6
DOIs
Publication statusPublished - 1 Sep 2011

Keywords

  • Jaro distance
  • Jaro-Winkler distance
  • Levenshtein distance
  • Metaphone
  • Neural network
  • Ranking First Hitting Rate
  • Word 2-gram
  • Word correction algorithms

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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