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    Matsuhara, Masafumi Sugawara, Taichi Chakraborty, Goutam and Mabuchi, Hiroshi 2015. 2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST). p. 49.

    Stoop, Wessel and van den Bosch, Antal 2014. Improving word prediction for augmentative communication by using idiolects and sociolects. Dutch Journal of Applied Linguistics, Vol. 3, Issue. 2, p. 136.


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    Matsuhara, Masafumi and Suzuki, Satoshi 2012. Effectiveness of Context-Aware Character Input Method for Mobile Phone Based on Artificial Neural Network. Applied Computational Intelligence and Soft Computing, Vol. 2012, p. 1.


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    Tanaka-Ishii, Kumiko 2007. Text Entry Systems.


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Word-based predictive text entry using adaptive language models

  • KUMIKO TANAKA-ISHII (a1)
  • DOI: http://dx.doi.org/10.1017/S1351324905004080
  • Published online: 01 February 2006
Abstract

The recent scaling down of mobile device form factors has increased the importance of predictive text entry. It is now also becoming an important communication tool for the disabled. Techniques related to predictive text entry software are discussed in a generalized, language-independent manner. The essence of predictive text entry is twofold, consisting of (1) the design of codes for text entry, and (2) the use of adaptive language models for decoding. Code design is examined in terms of the information-theoretical efficiency. Four adaptive language models are introduced and compared, and experimental results on text entry with these models are shown for English, Thai and Japanese.

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Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
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