Collaborative research: Bayesian methods for learning and analyzing natural languages

语言是人类行为中最复杂的方面之一，它为各种社会交往提供了基础。人们如何学习和使用语言的问题是包括认知科学、心理学和语言学在内的几个行为科学中广泛研究的主题。使用正式方法来探索这个问题的答案有很长的传统，最近的工作已经开始强调统计模型的重要性。在国家科学基金会的支持下，加州大学伯克利分校的格里菲斯博士和布朗大学的约翰逊博士将开发和研究基于贝叶斯统计的学习和分析自然语言的新方法和模型。在贝叶斯统计中，语言数据提供的关于语言结构的信息与约束所考虑的结构的“先验”分布相结合。这种方法可以更容易地从有限的数据中学习一种语言的性质，并与强调限制在学习中的作用的人类语言习得理论有直接联系。本研究旨在将用于学习和分析语言的统计模型与现代贝叶斯统计学中的两种方法相结合：马尔可夫链蒙特卡罗算法和非参数贝叶斯模型。这些方法使得将贝叶斯推理应用于人们通常在认知科学和语言学中使用的那种复杂模型成为可能。这个项目的结果将提供使用传统语言模型的新方法，并导致可能与解释人们如何获得语言有关的新模型。通过探索如何将现代统计方法应用于计算语言学中使用的概率模型，该项目将在统计学、语言学和认知科学之间建立更紧密的联系，并为学生提供在这些学科交叉的主题上接受培训的机会。该奖项是2006财年数学科学优先领域数学社会科学和行为科学(MSBS)特别竞赛的一部分。

Language is one of the most complex aspects of human behavior, and provides the foundation for many kinds of social interaction. The question of how people learn and use language is a subject of extensive research in several behavioral sciences, including cognitive science, psychology, and linguistics. There is a long tradition of using formal approaches to explore answers to this question, and recent work has begun to emphasize the importance of statistical models. With support from the National Science Foundation, Dr. Griffiths at UC Berkeley and Dr. Johnson at Brown University will develop and investigate new methods and models for learning and analyzing natural languages based on Bayesian statistics. In Bayesian statistics, the information about the structure of language provided by linguistic data is combined with a "prior" distribution that constrains the structures under consideration. This approach can make it easier to learn the properties of a language from limited amounts of data, and has a direct connection to theories of human language acquisition that emphasize the role of constraints in learning. This research project aims to integrate the statistical models used for learning and analyzing language with two methods from modern Bayesian statistics: Markov chain Monte Carlo algorithms and nonparametric Bayesian models. These methods make it possible to apply Bayesian inference in complex models of the kind that people typically work with in cognitive science and linguistics. The results of this project will provide new ways of working with traditional models of language, and lead to new models that are potentially of relevance to explaining how people acquire language. By exploring how contemporary statistical methods can be applied to the probabilistic models used in computational linguistics, this project will build closer connections between statistics, linguistics, and cognitive science, and provide opportunities for students to receive training in topics at the intersection of these disciplines.This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).