CI-P: Deep Understanding Resources

深度语言理解包括将语言映射到一种形式主义，这种形式主义在上下文中捕捉其预期意义，在支持推理的本体中使用概念和关系。人们普遍认为，除非在非常有限的领域，否则很难实现深刻的理解。因此，该领域的大部分研究都转向了所谓的浅层方法。近年来，由于大量带注释的语料库和现成的软件的可用性，浅层语言处理的工作得到了极大的增强。如果必须从头开始构建这个基础设施，那么该领域的大多数当前工作都是不可行的。尽管浅层语言理解取得了成功，但是，例如，不能使用现成的浅层组件构建复杂的对话系统或智能系统的NL接口（例如，人机交互）。对于这些和许多其他应用，我们需要更深入的了解。然而，目前还没有资源或工具包来支持深度理解。这项计划拨款的重点是确定研究界认为最有用的资源，并探索对这些资源有用的api（例如，深度语义解析器、语义词典和本体、话语处理能力，如基于本体的参考解析、表面言语行为解释）。感兴趣的社区分为两大阵营。第一类是我们可能称之为技术用户的人，他们想要使用深度理解能力，但他们的研究兴趣在智能系统的其他领域（例如，推理、学习、图像解释、机器人）。第二个阵营是技术开发人员，由NLP研究人员组成，他们正在进一步推动NLP研究。第二种群体的关键组成部分是学习成为自然语言研究人员和技术开发人员的学生。通过一系列案例研究和研讨会，我们探讨了针对不同类型的用户和不同应用程序的不同交付机制和需求的可行性。

Deep language understanding involves mapping language to a formalism that captures its intended meaning in context, using concepts and relations in an ontology that supports reasoning. It is generally thought that deep understanding is too difficult to accomplish except in very limited domains. As a result, most of the field has shifted to studying so-called shallow methods. Work in shallow language processing has been greatly enhanced in recent years due to the availability of considerable annotated corpora and off-the-shelf software. If one had to build this infrastructure from scratch, then most current work in the field would not be feasible. Despite the successes of shallow language understanding, however, one cannot, for instance, build sophisticated dialogue systems or NL interfaces to intelligent systems, (e.g., humanrobot interaction) using off-the-shelf shallow components. For these and many other applications, we need deeper understanding. However, there are currently no resources or toolkits to support deep understanding. This planning grant focuses on identifying the resources that the research community would find most useful and exploring what would be useful APIs to this resource (e.g., deep semantic parsers, semantic lexicons and ontologies, discourse processing capabilities such as ontology-based reference resolution, surface speech act interpretation). The interested communities fall into two broad camps. The first are what we might call technology users, people who want to use deep understanding capabilities but whose research interests are in some other area of intelligent systems (e.g., reasoning, learning, image interpretation, robotics). The second camp, technology developers, consist of researchers in NLP who are pushing research in NLP further. A critical subpart of this second group are students studying to become natural language researchers and technology developers. Through a series of case studies and a workshop, we explore the viability of different delivery mechanisms and needs for the different types of users and for different applications.