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A study on text auto completion for easing search of information

Author Affiliations

  • 1Bhilai Institute of Technology, Durg Department of Computer Science and Engineering, Durg, CG, India
  • 2Bhilai Institute of Technology, Durg Department of Computer Science and Engineering, Durg, CG, India

Res. J. Computer & IT Sci., Volume 5, Issue (5), Pages 12-14, July,20 (2017)

Abstract

Text prediction has multi-dimensional applications, ranging from easier database access to resolving of cognitive disabilities. The tools for such applications exist, but are not tolerant enough of error to cater the needs of masses. Moreover, these systems depend upon long duration initial queries which are used to build word lists that aid in query expansion. Such systems do increase their vocabulary of generic words used by users but cannot aid in formulation of words that are rarely used. In this paper, various factors affecting auto-complete, the aid provided by it to the dyslexic patients, its advantages and application have been discussed. In every domain where lexicography has to dealt with as an extension of information retrievals, auto-complete is a necessary tool for assisting people with query generation, so that any inherent cognitive disability or lack of proper vocabulary does not affect the retrieval of information

References

  1. Valitutti A., Toivonen H., Gross O. and Toivanen J.M. (2012)., Decomposition and Distribution of Humorous Effect in Interactive Systems., In AAAI Fall Symposium: Artificial Intelligence of Humor, 96-100.
  2. Ward D., Hahn J. and Feist K. (2012)., Autocomplete as a research tool: a study on providing search suggestions., Information Technology and Libraries, 31(4), 6.
  3. Berget G. and Sandnes F.E. (2015)., Do autocomplete functions reduce the impact of dyslexia on information-searching behavior? The case of Google., Journal of the Association for Information Science and Technology, 67(10), 2320-2328.
  4. Trnka K., Yarrington D., McCaw J., McCoy K.F. and Pennington C. (2007)., The effects of word prediction on communication rate for AAC., In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers. Association for Computational Linguistics, 173-176.
  5. Paek T., Lee B. and Thiesson B. (2009)., Designing phrase builder: A mobile real-time query expansion interface., In Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, 7.
  6. White R.W. and Marchionini G. (2007)., Examining the effectiveness of real-time query expansion., Information Processing & Management, 43(3), 685-704.
  7. Khoussainova N., Kwon Y., Balazinska M. and Suciu D. (2010)., Snip Suggest: Context-aware autocompletion for SQL., Proceedings of the VLDB Endowment, 4(1), 22-33.