Introduction to algorithms
Non-keyboard QWERTY touch typing: a portable input interface for the mobile user
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
A design principles of a weighted finite-state transducer library
Theoretical Computer Science - Special issue on implementing automata
Predicting text entry speed on mobile phones
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Entering text with a four-button device
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Semantic knowledge in word completion
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Investigating five key predictive text entry with combined distance and keystroke modelling
Personal and Ubiquitous Computing
Predictive text entry for agglutinative languages using unsupervised morphological segmentation
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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A cluster keyboard partitions the letters of the alphabet onto subset keys. On such keyboards most words are typed with no more key presses than on the standard keyboard, but a key sequence may stand for two or more words. In current practice, this ambiguity problem is addressed by hypothesizing words according to their unigram (occurrence) frequency. When the hypothesized word is not the intended one, an error arises. In this paper, we study the effect of deploying large, n-gram language models used in speech recognition for improving the error rate. We use the North American Business News (NAB) corpus, which contains hundreds of millions of words. We report on results for the telephone keypad and for cluster keyboards with 5, 8, 10, and 14 keys based on the QWERTY layout. Despite our assumption that a word hypothesis must be displayed promptly, we show that the error rate can be reduced to up to one-fourth of the rate of the unigram method.