Predictive Text Entry for Agglutinative Languages Using Unsupervised Morphological Segmentation

  • Miikka Silfverberg
  • Krister Lindén
  • Mirka Hyvärinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7182)

Abstract

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.

Keywords

Mobile Phone Hide Markov Model Word Form Training Corpus Letter Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miikka Silfverberg
    • 1
  • Krister Lindén
    • 1
  • Mirka Hyvärinen
    • 1
  1. 1.Department of Modern LanguagesUniversity of HelsinkiHelsinkiFinland

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