Synergies in Computational Models of Morpho-phonological Learning

形态语音学习计算模型的协同作用

• 批准号: 0544127
• 负责人: Mark Johnson
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0544127&HistoricalAwards=false
• 关键词: Synergies; Computational; Models; Morpho; phonological

Improving our understanding of how children learn language has profound implications for a variety of fields, ranging from nature versus nurture debates to therapies for treating language impairments. Learning a language involves learning a wide variety of different things, including the words of the language and the way they combine to form phrases and sentences, as well as the distribution of sounds of the language. Little is known about the way that all these aspects of language learning interact, and the goal of this research is to identify which aspects are likely to interact and whether this interaction is synergistic. With support from the National Science Foundation's programs in Linguistics and in Human Language & Communication, Dr. Mark Johnson and Dr. Katherine Demuth will use Bayesian statistics to construct a variety of mathematical models of the language acquisition process, and by comparing these to experimental data, identify the most important synergistic interactions involved in language learning.

提高我们对儿童如何学习语言的理解对许多领域都有深远的影响，从先天与后天的争论到治疗语言障碍的疗法。学习一门语言包括学习各种不同的东西，包括语言中的单词和它们组合成短语和句子的方式，以及语言的发音分布。关于语言学习的所有这些方面是如何相互作用的，我们知之甚少，本研究的目的是确定哪些方面可能相互作用，以及这种相互作用是否具有协同作用。在美国国家科学基金会语言学和人类语言交流项目的支持下，Mark Johnson博士和Katherine Demuth博士将使用贝叶斯统计来构建语言习得过程的各种数学模型，并通过将这些模型与实验数据进行比较，确定语言学习中最重要的协同作用。