Learning to Perform Knowlege Intensive Inferences

本研究的目标是研究一个集成的学习理论，知识表示和推理，并评估它在大规模的知识密集型推理在自然语言领域。 最近的研究，在学习推理的框架内，已经表明，有很多从研究这些问题在一个统一的框架内获益。 本研究探讨了这个框架内的一些基本问题-集中在概率设置。重点是开发算法，利用放松一些“传统”的假设在这一领域。这些包括对学习算法的要求（例如，学习“好”的密度估计），推理算法（例如，支持 * 所有 * 查询一致好）和在这个领域研究的一些知识表示。 对这些理论在自然语言领域的应用进行了实验研究和评估。 重点是学习方法和表示相结合的低级学习算法，以执行更高级别的推理。 这项研究将对理解结合学习和推理所涉及的一些基本问题产生影响，并将允许在弥合自然语言领域的低层次工作和更高层次目标之间的差距差距方面取得具体进展。 http://L2R.cs.uiuc.edu/~danr/Grants/nsf98.ht www.example.com

The goal of this research is to study an integrated theory of learning, knowledge representation and reasoning and evaluate it on large scale knowledge intensive inferences in the natural language domain. Recent studies, within the Learning to Reason framework, have shown that there is much to gain from studying these issues within a unified framework. This research investigates some of the fundamental issues within this framework -- concentrating on a probabilistic setting. The emphasis is on developing algorithms that exploit the relaxation of some of the ``traditional'' assumptions in this domain. These include requirements put on the learning algorithms (e.g., learn a ``good'' density estimation), reasoning algorithms (e.g., support *all* queries uniformly well) and on some of the knowledge representations studied in this domain. Application of these theories to the natural language domain are studied and evaluated experimentally. The emphasis is on learning methods and representations for combining lowlevel learning algorithms to perform higher level inferences. This research will have impact both on understanding some of the fundamental issues involved in combining learning and reasoning and will allow for making concrete progress towards bridging the gap between the low-level work and higher level goals in the natural language domain. http://L2R.cs.uiuc.edu/~danr/Grants/nsf98.ht ml