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.