CAREER: Learning Coherent Concepts: Theory and Applications to Natural Language

这是一个为期4年的连续奖励的第一年。本研究探讨的学习情境中，多个学习者共存，可能会学习不同的功能，在同一输入，但有相互兼容的限制，他们的成果。PI将为这些情况开发学习理论，并将研究在自然语言推理中利用它们的算法方法。理论研究将集中于开发一致性条件的语义，并从学习理论的角度对其进行研究，目的是了解在这些情况下学习如何变得更容易和更强大。算法的研究将集中在开发的方法来利用一致性，并将有一个显着的实验组件，使用浅解析的问题作为一个测试平台，调查链接的一致性分类器和推理，依赖于几个分类器的结果这项研究将有一个显着的影响，在学习的理论研究和我们的能力，以执行更高层次的推理自然语言。这将有助于解决预测的学习困难与认知系统学习的明显轻松之间的对比。此外，它将提供如何利用一致性的理解，以便为这些情况开发更好的学习和推理方法，并将导致各种浅层解析任务的综合学习方法，使用SNoW学习架构以大规模的方式实现和演示。将相互作用的分类器的理解以及依赖于几个分类器执行推理的方法扩展到SNoW中将直接适用于该领域的各种其他任务。http://L2R.cs.uiuc.edu/~danr/Grants/eareerOO.html

This is the first year of funding of a 4-year continuing award. This research investigates learning scenarios in which multiple learners co-exist and may learn different functions on the same input, but there are mutual compatibility constraints on their outcomes. The PI will develop a learning theory for these situations, and will study algorithmic ways to exploit them in natural language inference. The theoretical study will concentrate, on developing a semantics for the coherency conditions and study it from a learning theory point of view with a goal of understanding in what ways learning becomes easier and more robust in these situations. The algorithmic study will concentrate on developing ways to exploit coherency and will have a significant experimental component, using the problem of shallow parsing as a testbed for investigating chaining of coherent classifiers and inferences that rely on the outcomes of several classifiers This research will have a significant impact on theoretical research in learning and on our ability to perform higher level inference in natural language. It will help to resolve the contrast between the predicted hardness of learning and the apparent ease at which cognitive systems learn. Moreover, it will provide an understanding of how to exploit coherency in order to develop better learning and inference methods for these situations, and will result in an integrated learning approach to a variety of shallow parsing tasks, implemented and demonstrated in a large scale manner using the SNoW learning architecture. Incorporating the understanding of interacting classifiers, as well as methods to perform inferences that rely on several classifiers, into SNoW will be directly applicable to a variety of other tasks in this domain. http://L2R.cs.uiuc.edu/~danr/Grants/eareerOO.html