CAREER: Understanding Negation in Positive Terms

否定的表达是所有人类语言的重要特征。然而，尽管否定无处不在，但目前的智能系统很难理解它的影响，而人们很少这样做。其中最重要的一点是，事实否定通常传达一些积极的意义。例如，对“约翰早上出门时手中从不拿着咖啡”的自然解释表明，约翰确实在早上出门，而且他总是手里拿着一杯咖啡。这个项目建立了新的资源和算法，使智能系统能够理解包含否定的句子中隐藏的大量积极解释。这些资源和算法有可能使对话系统能够进行更自然的对话，改进机器翻译，并使几乎任何需要文本理解的最终用户应用程序受益。除了基础研究，该项目还将支持不同的博士和本科生群体，以及北得克萨斯大学的跨学科研讨会、博士生国际研究停留、研讨会和行业拓展活动。该职业项目的技术目标分为三个方面。第一个主旨创建了一个全面的否定及其积极解释语料库，包括几个领域和体裁的所有否定类型。为此，研究小组探索了一些策略，以说明粒度的程度，最大限度地减少注释工作，并对潜在的积极解释进行评分和重写。第二个重点是开发计算算法，从否定中自动产生积极的解释，并学习语境和话语的表征，然后利用它们来识别否定的焦点，并用积极的对应物重写它们。第三个重点是外部评估，通过将否定纳入识别蕴涵和推断空间时间轴的任务中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.