Doctoral Dissertation Research: Discovering Semantic Primitives

博士论文研究：发现语义原语

基本信息

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

项目摘要

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.

语言中的许多单词都有其含义，需要丰富且结构化的表征系统。例如，“大多数”的含义比较两个集合的相对大小：如果快乐的音乐家多于不快乐的音乐家，则“大多数音乐家都很快乐”这样的句子为真。然而，支持这些类型的集合比较的表征系统还没有被很好地理解。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）。当前的项目旨在发现这些类型的复杂和抽象词义的表征系统。认知科学的许多结果发现，人们倾向于学习对于表征系统来说更简单的概念：人们发现更容易学习像这样的概念“黑色椅子”的例子比“短而黑色的椅子”更复杂。当前的项目将向人们传授新颖的、类似语言的概念。它将使用一种新颖的计算模型来预测人们在学习实验中应该根据不同可能的表征理论做出什么概括，假设表征“简单性”影响学习。这将允许比较多个表征系统，以确定哪个最适合人类的学习模式。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含义的方式非常重要。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。