认知科学中的人类智能与机器价值——设计思维方法

Akshaya V S, Beatriz Lúcia Salvador Bizotto, Mithileysh Sathiyanarayanan
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引用次数: 1

摘要

潜在语义分析(Latent Semantic Analysis, LSA)是一种应用于大量文本语料库的统计评估或建模来表达和提取文本含义的方法,它的发展是本研究理解设计思维方法的主要动机。我们介绍了LSA,并给出了一些实例,说明如何使用LSA来进一步了解我们的认知和开发实用技术。自从LSA诞生以来,其他用于文本语料库意义检测和分析的统计模型已经被创建、测试和改进。本研究证明了语义学统计模型对认知科学研究和认知发展的价值。这些模型特别有用,因为它们使研究人员能够使用人类智能的扩展表示来研究与知识、话语感知、文本认知和语言有关的广泛问题。
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Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach
Latent Semantic Analysis (LSA) is an approach used for expressing and extracting textual meanings using statistical evaluations or modeling applied to vast corpora of text, and its development has been a major motivation for this study to understand the design thinking approach. We introduced LSA and gave some instances of how it might be used to further our knowledge of cognition and to develop practical technology. Since LSA's inception, other alternative statistical models for meaning detection and analysis in text corpora have been created, tested, and refined. This study demonstrates the value that statistical models of semantics provide to the study of cognitive science and the development of cognition. These models are particularly useful because they enable researchers to study a wide range of problems pertaining to knowledge, discourse perception, text cognition, and language using expansive representations of human intelligence.
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