Entry Sharma:2014:WPS from talip.bib

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BibTeX entry

@Article{Sharma:2014:WPS,
  author =       "Manoj Kumar Sharma and Debasis Samanta",
  title =        "Word Prediction System for Text Entry in {Hindi}",
  journal =      j-TALIP,
  volume =       "13",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jun,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2617590",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Fri Jun 20 18:22:19 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/spell.bib;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "Word prediction is treated as an efficient technique
                 to enhance text entry rate. Existing word prediction
                 systems predict a word when a user correctly enters the
                 initial few characters of the word. In fact, a word
                 prediction system fails if the user makes errors in the
                 initial input. Therefore, there is a need to develop a
                 word prediction system that predicts desired words
                 while coping with errors in initial entries. This
                 requirement is more relevant in the case of text entry
                 in Indian languages, which are involved with a large
                 set of alphabets, words with complex characters and
                 inflections, phonetically similar sets of characters,
                 etc. In fact, text composition in Indian languages
                 involves frequent spelling errors, which presents a
                 challenge to develop an efficient word prediction
                 system. In this article, we address this problem and
                 propose a novel word prediction system. Our proposed
                 approach has been tried with Hindi, the national
                 language of India. Experiments with users substantiate
                 43.77\% keystroke savings, 92.49\% hit rate, and
                 95.82\% of prediction utilization with the proposed
                 word prediction system. Our system also reduces the
                 spelling error by 89.75\%.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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