Learning and Intelligent Systems: Optimization in Language and Language Learning

该项目由学习和智能系统倡议资助。它是一门跨学科的研究，涉及语言的知识、处理和学习。它从一个框架，利用数理统计，自适应系统和形式学习理论的结果，提供了一种手段，治疗语言作为一种统计优化。 首席研究员先前在神经网络中整合语言学理论与优化原则的工作导致了这种新的语法形式主义，即优化理论，它对人类语言研究的许多方面产生了相当大的影响，包括学习。最近发展起来的心理学实验方法现在为语言学习过程提供了可靠的数据，甚至在婴儿中也是如此。 这项研究汇集了观察实时处理和学习语言的实验方法，优化和自动语言处理研究的计算方法，以及研究人类语言基本表征结构的语言学方法。 研究人员不仅带来了贡献学科的专业知识，而且在跨学科合作方面也有相当丰富的经验。 这项研究的结果将帮助我们解释人类--可能还有人工系统--如何学习使用和理解语言的奥秘。

This project is being funded through the Learning and Intelligent Systems (LIS) initiative. It is interdisciplinary research in the knowledge, processing, and learning of language. It proceeds from a framework utilizing results from mathematical statistics, adaptive systems, and formal learning theory which provide a means of treating language as a kind of statistical optimization. Previous work by the principal investigator on the integration of linguistic theory with optimization principles in neural networks has led to this new grammar formalism, optimality theory, which has had considerable impact on many aspects of the study of human language, including learning. Recently developed methods of psychological experimentation now provide reliable data on the process of language learning, even in infants. This research brings together these experimental methods for observing real-time processing and learning of language, computational methods of research on optimization and automatic language processing, and linguistic methods for studying the structure of the representations essential for human language. The investigators bring not only expertise in the contributing disciplines, but also considerable experience in interdisciplinary collaboration. The results of this research will help us to explain the mystery of how humans - and possibly artificial systems - can learn to use and understand languages.