Text input disambiguation supported on a hierarchical user model
Authors: 
Carlos Bento, Nuno GilAuthors Info & Claims
Carlos Bento, Nuno GilAuthors Info & ClaimsPages 253 - 258
Abstract
Mobile phones are used for various tasks that go far from voice communication. A popular use is for composition of short messages (SMSs), but other applications are also available like email, agenda, contact, and note management. All these uses have in common the need for text input on a small keyboard with ambiguity problems.Various techniques are currently used for input disambiguation, with variable results in terms of usability and efficiency. Some techniques achieve good performance with messages composed by words from a dictionary but poorly when a significant number of words are not in memory.In this paper we present a solution for text disambiguation supported on a general model for disambiguation plus a user model generated from previous messages sent by the user. We present results obtained with this approach and discuss future improvements.
References
[1]
Poika Isokoski (2004). Manual Text Input: Experiments, Models, and Systems. Academic Dissertation, Faculty of Information Sciences of the University of Tampere, April 23rd, 2004. A-2004-3 Tampere, ISBN 951-44-5959-9.
[2]
J. Cardinal and S. Langerman (2005). Designing small keyboards is hard. Theoretical Computer Science, 332:405--415.
[3]
T9: Learn more. Tegic Communications, Inc. 30 April 2005 http://www.t9.com/learn.html.
[4]
eZiText word prediction. Zi Corporation, 30 April 2005 http://www.zicorp.com/ezitext.htm.
[5]
Whaley D. (2002). Better Ways of Typing Text Messages on your Hand-held Device, in Business Briefing: Wireless Technology 2003, p. 1--3
[6]
MacKenzie, S., Kober, H., Smith, D., Jones, T., Skepner, E. (2001). LetterWise: prefix-based disambiguation for mobile text input, in Proceedings of the 14th annual ACM symposium on User interface software and technology, Orlando, ACM, p. 111--120.
[7]
Pavlovych, A., Stuerzlinger, W. (2003). Less-Tap: A fast and easy-to-learn text input technique for phones, in Proceedings of Graphics Interface, in GI 2003, Canadian Information Processing Society, p. 97--104.
[8]
Hasselgren, J., Montnemery, E., Nugues, P., Svensson, M. (2003). HMS: A Predictive Text Entry Method Using Bigrams, in Proceedings of the Workshop on Language Modeling for Text Entry Methods, 10th Conference of the European Chapter of the Association of Computational Linguistics, Budapest, p. 43--49.
[9]
Wigdor, D., Balakrishnan, R. (2004). A Comparison of Consecutive and Concurrent Input Text Entry Techniques for Mobile Phones, in Proceedings of CHI 2004, Volume 6. Number 1, Vienna, ACM, p. 81--88.
[10]
Wigdor, D., Balakrishnan, R. (2003). TiltText: using tilt for text input to mobile phones, in Proceedings of the 16th annual ACM symposium on User interface software and technology. Vancouver, ACM, p. 81--90.
[11]
Gutowitz, H. (2003). Barriers to Adoption of Dictionary- Based Text-Entry Methods: A Field Study, in 10th Conference of the European Chapter of the Association for Computational Linguistics, Budapest.
[12]
MacKenzie, S. (2002). KSPC (Keystrokes per Character) as a Characteristic of Text Entry Techniques, in Proceedings of the 4th International Symposium on Mobile Human-Computer Interaction, Springer-Verlag, p. 195--210.
[13]
On-line statistics, University of Leicester. 30 April 2005 http://www.le.ac.uk/biology/gat/virtualfc/Stats/spear.htm
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Published: 12 October 2005
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