The Dynamics of Probabilistic Grammar

概率语法的动力学

• 批准号: 0624345
• 负责人: Joan Bresnan
• 金额: $ 74.98万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-12-15 至 2010-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0624345&HistoricalAwards=false
• 关键词: Dynamics; Probabilistic; Grammar

The use of language is central to human social dynamics. Current techniques for modeling human language are widely based on simplifying assumptions that underestimate human language capacities by ignoring statistical patterns. The present project starts from a strongly contrasting approach which can lead to a breakthrough in the understanding of linguistic dynamics by linking changes in language across the different time scales of speech, the human life cycle, and the history of communities. This approach models grammar as a highly plastic cognitive system sensitively tuned to the probabilities of the environment. The project combines detailed statistical analyses of patterns found in recorded speech with laboratory experiments on language production in adults and language learning in children. The phenomena considered include high-level grammatical choices (such as whether to say "give someone a job" or "give a job to someone"), low-level pronunciation choices (such as where "to" is reduced to "tuh"), and overregularizations (such as when children say "goed" instead of "went"). Studies will involve work with data from multiple dialects of English, as well as German and Chinese. The project involves cross-disciplinary collaboration among researchers trained in Linguistics, Psychology, and Computer Science. This research will lay the groundwork for improved language technologies, and may find other applications, for example in the treatment of language disorders.

语言的使用是人类社会动态的核心。目前的人类语言建模技术普遍建立在简化假设的基础上，这些假设忽略了统计模式，低估了人类的语言能力。本项目从一种强烈对比的方法开始，通过将语言在不同时间尺度、人类生命周期和社区历史上的变化联系起来，可以在理解语言动态方面取得突破。这种方法将语法建模为一个高度可塑的认知系统，对环境的可能性敏感。该项目结合了对语音记录模式的详细统计分析，以及对成人语言产生和儿童语言学习的实验室实验。考虑的现象包括高级语法选择（例如是否说“给某人一份工作”或“给某人一份工作”），低级发音选择（例如“to”在哪里被简化为“tuh”）和过度正则化（例如当孩子说“goed”而不是“went”）。研究将涉及到来自多种英语方言、德语和汉语的数据。该项目涉及语言学、心理学和计算机科学领域的研究人员之间的跨学科合作。这项研究将为改进语言技术奠定基础，并可能找到其他应用，例如在语言障碍的治疗中。