The expression of negation is an important feature of all human languages. However despite the pervasiveness of negation, current intelligent systems have great difficulty comprehending its effects, whereas people rarely do. One of the most important of these is the fact negation generally conveys some positive meaning. For example, a natural interpretation of "John never leaves the house in the morning without a coffee in his hand" suggests that John does leave the house in the morning and that he always does so with a coffee in his hand. This project builds novel resources and algorithms to enable intelligent systems to understand the numerous positive interpretations hidden in sentences containing negation. These resources and algorithms have the potential to empower dialogue systems to carry on more natural conversations, improve machine translation, and benefit virtually any end-user application requiring text understanding. Beyond fundamental research, this project will support a diverse cohort of PhD and undergraduate students as well as an interdisciplinary seminar at University of North Texas, international research stays for PhD students, workshops, and industry outreach activities. The technical aims of this CAREER project are divided into three thrusts. The first thrust creates a comprehensive corpus of negations and their positive interpretations, including all negations types in several domains and genres. To this end, the research team explores strategies to account for degrees of granularity, to minimize the annotation effort and to score and rewrite potential positive interpretations. The second thrust is to develop computational algorithms to automatically generate positive interpretations from negation, and to learn representations of context and discourse, and then leverage them to identify the foci of negation and rewrite them with positive counterparts. The third thrust consists of extrinsic evaluation, by incorporating negation into the tasks of recognizing entailments and inferring spatial timelines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.