Predictive text entry using syntax and semantics

Sebastian Ganslandt, Jakob Jörwall, Pierre Nugues

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


Most cellular telephones use numeric keypads, where texting is supported by dictionaries and frequency models. Given a key sequence, the entry system recognizes the matching words and proposes a rank-ordered list of candidates. The ranking quality is instrumental to an effective entry.

This paper describes a new method to enhance entry that combines syntax and language models. We first investigate components to improve the ranking step: language models and semantic relatedness. We then introduce a novel syntactic model to capture the word context, optimize ranking, and then reduce the number of keystrokes per character (KSPC) needed to write a text. We finally combine this model with the other components and we discuss the results.

We show that our syntax-based model reaches an error reduction in KSPC of 12.4% on a Swedish corpus over a baseline using word frequencies. We also show that bigrams are superior to all the other models. However, bigrams have a memory footprint that is unfit for most devices. Nonetheless, bigrams can be further improved by the addition of syntactic models with an error reduction that reaches 29.4%.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Parsing Technologies (IWPT '09)
Publication statusPublished - 2009

Subject classification (UKÄ)

  • Electrical Engineering, Electronic Engineering, Information Engineering
  • Computer Science


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