论文标题
单词嵌入本质上恢复了人类思想的概念组织
Word Embeddings Inherently Recover the Conceptual Organization of the Human Mind
论文作者
论文摘要
机器学习是一种从丰富的数据来源中发现深层模式的一种手段。在这里，我们发现机器学习可以恢复人类思想的概念组织，将人类的思想应用于数百万人的自然语言使用。利用数十亿个网页中的文本，我们恢复了大规模单词协会网络中代表的英语，荷兰语和日语中包含的大多数概念。我们的结果证明了机器学习是一种探究人类思想的手段的合理性，该手段是使用自我报告和观察方法无法实现的深度和规模。除了直接的心理应用外，我们的方法可能对与在任何科学领域定义，评估，关联或发现概念的项目相关的项目可能有用。
Machine learning is a means to uncover deep patterns from rich sources of data. Here, we find that machine learning can recover the conceptual organization of the human mind when applied to the natural language use of millions of people. Utilizing text from billions of webpages, we recover most of the concepts contained in English, Dutch, and Japanese, as represented in large scale Word Association networks. Our results justify machine learning as a means to probe the human mind, at a depth and scale that has been unattainable using self-report and observational methods. Beyond direct psychological applications, our methods may prove useful for projects concerned with defining, assessing, relating, or uncovering concepts in any scientific field.
