LetterWise: prefix-based disambiguation for mobile text input
Proceedings of the 14th annual ACM symposium on User interface software and technology
KSPC (Keystrokes per Character) as a Characteristic of Text Entry Techniques
Mobile HCI '02 Proceedings of the 4th International Symposium on Mobile Human-Computer Interaction
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Unsupervised models for morpheme segmentation and morphology learning
ACM Transactions on Speech and Language Processing (TSLP)
Morph-based speech recognition and modeling of out-of-vocabulary words across languages
ACM Transactions on Speech and Language Processing (TSLP)
Word n-grams for cluster keyboards
TextEntry '03 Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods
A probabilistic mobile text entry system for agglutinative languages
IEEE Transactions on Consumer Electronics
| Hi-index | 0.00 |
Systems for predictive text entry on ambiguous keyboards typically rely on dictionaries with word frequencies which are used to suggest the most likely words matching user input. This approach is insufficient for agglutinative languages, where morphological phenomena increase the rate of out-of-vocabulary words. We propose a method for text entry, which circumvents the problem of out-of-vocabulary words, by replacing the dictionary with a Markov chain on morph sequences combined with a third order hidden Markov model (HMM) mapping key sequences to letter sequences and phonological constraints for pruning suggestion lists. We evaluate our method by constructing text entry systems for Finnish and Turkish and comparing our systems with published text entry systems and the text entry systems of three commercially available mobile phones. Measured using the keystrokes per character ratio (KPC) [8], we achieve superior results. For training, we use corpora, which are segmented using unsupervised morphological segmentation.