Natural touch interfaces, common now in devices such as tablets and smartphones, make it cumbersome for users to select text. There is a need for a new text selection paradigm that goes beyond the high acuity selection-by-mouse that we have relied on for decades. In this paper, we introduce such a paradigm, called Smart Selection, which aims to recover a user’s intended text selection from her touch input. We model the problem using an ensemble learning approach, which leverages multiple linguistic analysis techniques combined with information from a knowledge base and aWeb graph. We collect a dataset of true intended user selections and simulated user touches via a large-scale crowdsourcing task, which we release to the academic community. We show that our model effectively addresses the smart selection task and significantly outperforms various baselines and standalone linguistic analysis techniques.