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Measuring the Performance of a Location-Aware Text Prediction System

Measuring the Performance of a Location-Aware Text Prediction System Measuring the Performance of a Location-Aware Text Prediction System LU´S FILIPE GARCIA, Instituto Polit´ cnico de Beja I e LU´S CALDAS DE OLIVEIRA and DAVID MARTINS DE MATOS, INESC ID Lisboa, I Instituto Superior T´ cnico, Universidade de Lisboa e In recent years, some works have discussed the conception of location-aware Augmentative and Alternative Communication (AAC) systems with very positive feedback from participants. However, in most cases, complementary quantitative evaluations have not been carried out to confirm those results. To contribute to clarifying the validity of these approaches, our study quantitatively evaluated the effect of using language models with location knowledge on the efficiency of a word and sentence prediction system. Using corpora collected for three different locations (classroom, school cafeteria, home), location-specific language models were trained with sentences from each location and compared with a traditional all-purpose language model, trained on all corpora. User tests showed a modest mean improvement of 2.4% and 1.3% for Words Per Minute (WPM) and Keystroke Saving Rate (KSR), respectively, but the differences were not statistically significant. Since our text prediction system relies on the concept of sentence reuse, we ran a set of simulations with language models having different sentence knowledge http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Accessible Computing (TACCESS) Association for Computing Machinery

Measuring the Performance of a Location-Aware Text Prediction System

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1936-7228
DOI
10.1145/2739998
Publisher site
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Abstract

Measuring the Performance of a Location-Aware Text Prediction System LU´S FILIPE GARCIA, Instituto Polit´ cnico de Beja I e LU´S CALDAS DE OLIVEIRA and DAVID MARTINS DE MATOS, INESC ID Lisboa, I Instituto Superior T´ cnico, Universidade de Lisboa e In recent years, some works have discussed the conception of location-aware Augmentative and Alternative Communication (AAC) systems with very positive feedback from participants. However, in most cases, complementary quantitative evaluations have not been carried out to confirm those results. To contribute to clarifying the validity of these approaches, our study quantitatively evaluated the effect of using language models with location knowledge on the efficiency of a word and sentence prediction system. Using corpora collected for three different locations (classroom, school cafeteria, home), location-specific language models were trained with sentences from each location and compared with a traditional all-purpose language model, trained on all corpora. User tests showed a modest mean improvement of 2.4% and 1.3% for Words Per Minute (WPM) and Keystroke Saving Rate (KSR), respectively, but the differences were not statistically significant. Since our text prediction system relies on the concept of sentence reuse, we ran a set of simulations with language models having different sentence knowledge

Journal

ACM Transactions on Accessible Computing (TACCESS) – Association for Computing Machinery

Published: Jun 1, 2015

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