Mining, analyzing, and modeling text written on mobile devices

Document Type

Article

Publication Date

10-10-2019

Department

Department of Computer Science

Abstract

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.

Publisher's Statement

© Cambridge University Press 2019. Publisher's version of record: https://doi.org/10.1017/S1351324919000548

Supporting Data

Data supporting this paper can be accessed on Digital Commons @ Michigan Tech here: https://digitalcommons.mtu.edu/mobiletext/

Publication Title

Natural Language Engineering

Link to Full Text

Share

COinS