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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\%.
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