Doctoral Dissertation Research: Discovering Semantic Primitives

博士论文研究：发现语义原语

• 批准号: 1025309
• 负责人: Edward Gibson
• 金额: $ 1.2万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025309&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Discovering; Semantic

Many words in language have meanings which require a rich and structured representational system. For instance, the meaning of "most" compares the relative sizes of two sets: a sentence like "Most musicians are happy" is true if the happy musicians outnumber the unhappy musicians. However, the representational system which supports these types of set comparisons is not well-understood. Indeed, there are often multiple ways a computational system could realize such meanings. For "most," one might compare the number of happy musicians to the number of unhappy musicians, or to half the total number of musicians (e.g. Hackl 2009, Pietroski et al. 2009). The current project aims to discover the representational system for these types of complex and abstract word meanings.Many results in cognitive science have found that people are biased to learn concepts which are simpler for their representation system: people find it easier to learn a concept like "black chairs" from examples than the more complex "short and black chairs." The current project will teach people novel, language-like concepts. It will use a novel computational model to predict what generalizations people should make in the learning experiment according to different possible representational theories, under the assumption that representational "simplicity" influences learning. This will allow multiple representational systems to be compared to see which best fit human learning patterns. Uncovering the basic operations which underlie linguistic representation is important for understanding the way in which the mind learns and uses complex meanings. Moreover, the representational systems which best describe human learning are likely to provide a good basis for artificial systems which use and learn natural language like humans.

语言中的许多词都有意义，这需要一个丰富而结构化的表征系统。例如，“most”的意思比较了两个集合的相对大小：如果快乐的音乐家的数量超过不快乐的音乐家，那么像“Most musicians are happy”这样的句子就为真。然而，支持这些类型的集合比较的表征系统还没有得到很好的理解。事实上，计算系统通常有多种方式可以实现这些含义。对于“大多数”，人们可以将快乐的音乐家的数量与不快乐的音乐家的数量进行比较，或者与音乐家总数的一半进行比较（例如，Hackl 2009，Pietroski et al. 2009）。目前的项目旨在发现这些复杂和抽象词义的表征系统。认知科学的许多结果发现，人们倾向于学习对他们的表征系统来说更简单的概念：人们发现从例子中学习像“黑椅子”这样的概念比更复杂的“短黑椅子”更容易。“目前的项目将教人们新颖的，类似语言的概念。它将使用一种新的计算模型来预测人们在学习实验中应该根据不同的可能表征理论做出什么样的概括，假设表征的“简单性”影响学习。这将允许多个表征系统进行比较，以查看哪些最适合人类学习模式。揭示语言表征背后的基本操作对于理解大脑学习和使用复杂意义的方式非常重要。此外，最能描述人类学习的表征系统可能为像人类一样使用和学习自然语言的人工系统提供良好的基础。