Foundations of statistical natural language processing
Foundations of statistical natural language processing
Phrase sets for evaluating text entry techniques
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Text Entry Systems: Mobility, Accessibility, Universality
Text Entry Systems: Mobility, Accessibility, Universality
An overview of Microsoft web N-gram corpus and applications
HLT-DEMO '10 Proceedings of the NAACL HLT 2010 Demonstration Session
Text text revolution: a game that improves text entry on mobile touchscreen keyboards
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
A versatile dataset for text entry evaluations based on genuine mobile emails
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Performance comparisons of phrase sets and presentation styles for text entry evaluations
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
Complementing text entry evaluations with a composition task
ACM Transactions on Computer-Human Interaction (TOCHI)
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Text entry experiments evaluating the effectiveness of various input techniques often employ a procedure whereby users are prompted with natural language phrases which they are instructed to enter as stimuli. For experimental validity, it is desirable to control the stimuli and present text that is representative of a target task, domain or language. MacKenzie and Soukoreff (2001) manually selected a set of 500 phrases for text entry experiments. To demonstrate representativeness, they correlated the distribution of single letters in their phrase set to a relatively small (by current standards) corpus of English prior to 1966, which may not reflect the style of text input today. In this paper, we ground the notion of representativeness in terms of information theory and propose a procedure for sampling representative phrases from any large corpus so that researchers can curate their own stimuli. We then describe the characteristics of phrase sets we generated using the procedure for email and social media (Facebook and Twitter). The phrase sets and code for the procedure are publicly available for download.