Mining, analyzing, and modeling text written on mobile devices
Department of Computer Science
We present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type “in the wild” on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers.
Data supporting this paper can be accessed on Digital Commons @ Michigan Tech here: https://digitalcommons.mtu.edu/mobiletext/
Natural Language Engineering
Kristensson, P. O.
Mining, analyzing, and modeling text written on mobile devices.
Natural Language Engineering.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/934
© Cambridge University Press 2019. Publisher's version of record: https://doi.org/10.1017/S1351324919000548