iWordNet: A New Approach to Cognitive Science and Artificial Intelligence

Mark Chang, Monica Chang
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引用次数: 3

Abstract

One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term “understanding,” or the meaning of the word “meaning,” is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual’s knowledge and state of mind at the time of the network construction. As a result, “understanding” and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed “Path of Understanding” in an iWordNet that characterizes an individual’s understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual’s iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.
iWordNet:认知科学与人工智能的新途径
人工智能或计算语言学的主要挑战之一是理解单词或概念的含义。我们认为,由于不可避免的循环定义,“理解”一词的内涵或“意义”一词的含义仅仅是一个词映射游戏。当一个人定义一个概念时,这些循环定义就产生了,它的定义中的概念,等等,最终形成一个个性化的概念网络,我们称之为iWordNet。这样一个iWordNet作为一个人的知识和精神状态在网络建设时的外部表现。因此,“理解”和知识可以看作是iWordNet拓扑的一种可计算的统计属性。我们将讨论iWordNet的构建和分析,以及iWordNet中提出的“理解路径”,该路径表征个人对复杂概念(如书面文章)的理解。在我们对20名受试者的初步研究中,我们使用回归模型来证明个人iWordNet的拓扑特性与他的智商得分有关,这种关系表明iWordNet是研究认知科学和人工智能的潜在新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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