  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.
