Improving Word Prediction Using Markov Models and Heuristic Methods.

The goal of this project was to design and implement a new word predictor for Swedish that would suggest words that are more grammatically appropriate, thus presenting a lower cognitive load for users and saving significantly more keystrokes than the previous predictor. The new predictor that was de...

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Publicado en:AAC: Augmentative & Alternative Communication Vol. 17; no. 4; pp. 255 - 265
Autores principales: Hunnicutt, Sheri, Carlberger, Johan
Formato: Artículo
Publicado: Taylor & Francis Ltd Dec2001
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Improving Word Prediction Using Markov Models and Heuristic Methods.
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          Hunnicutt, Sheri
          Carlberger, Johan
      su:
        Linguistics
        Writing materials & instruments
        Markov processes
        Heuristic
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        subj:
          Linguistics
          Writing materials & instruments
          Markov processes
          Heuristic
      ab: The goal of this project was to design and implement a new word predictor for Swedish that would suggest words that are more grammatically appropriate, thus presenting a lower cognitive load for users and saving significantly more keystrokes than the previous predictor. The new predictor that was designed and developed uses a probabilistic language model based on the well-established ideas of the trigram predictor for speech recognition, developed by IBM. In tests, this program has been shown to result in keystroke savings of 46% given five predictions--a substantial saving compared with the 35% savings achieved with the previous predictor.
      pubtype: Academic Journal
      doctype: Article
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    language: English
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