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  • Cited by 9
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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    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.

    Liu, Chien-Liang Hsaio, Wen-Hoar Lee, Chia-Hoang and Chi, Hsiao-Cheng 2013. An HMM-Based Algorithm for Content Ranking and Coherence-Feature Extraction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 43, Issue. 2, p. 440.

    Matsuhara, Masafumi Itoh, Miki Chakraborty, Goutam and Mabuchi, Hiroshi 2013. 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013). p. 191.

    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.

    Matsuhara, Masafumi and Suzuki, Satoshi 2011. 2011 3rd International Conference on Awareness Science and Technology (iCAST). p. 318.

    Ouk, Phavy Ye Kyaw Thu, Matsumoto, Mitsuji and Yoshiyori Urano, 2008. 2008 IEEE Symposium on Visual Languages and Human-Centric Computing. p. 225.

    Phavy Ouk, Ye Kyaw Thu, Mitsuji Matsumoto, and Yoshiyori Urano, 2008. 2008 IEEE International Conference on Information Reuse and Integration. p. 214.

    Tanaka-Ishii, Kumiko 2007. Text Entry Systems.


Word-based predictive text entry using adaptive language models

  • DOI:
  • Published online: 01 February 2006

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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