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Mining, analyzing, and modeling text written on mobile devices

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Vertanen, K 
Kristensson, PO 

Abstract

jats:titleAbstract</jats:title>jats:pWe 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.</jats:p>

Description

Keywords

Language resources, Corpus linguistics, Statistical methods, Text data mining

Journal Title

Natural Language Engineering

Conference Name

Journal ISSN

1351-3249
1469-8110

Volume Title

27

Publisher

Cambridge University Press (CUP)

Rights

All rights reserved
Sponsorship
Engineering and Physical Sciences Research Council (EP/N014278/1)