  Categories such as animal or furniture are acquired at an early age and play
an important role in processing, organizing, and communicating world knowledge.
Categories exist across cultures: they allow to efficiently represent the
complexity of the world, and members of a community strongly agree on their
nature, revealing a shared mental representation. Models of category learning
and representation, however, are typically tested on data from small-scale
experiments involving small sets of concepts with artificially restricted
features; and experiments predominantly involve participants of selected
cultural and socio-economical groups (very often involving western native
speakers of English such as U.S. college students) . This work investigates
whether models of categorization generalize (a) to rich and noisy data
approximating the environment humans live in; and (b) across languages and
cultures. We present a Bayesian cognitive model designed to jointly learn
categories and their structured representation from natural language text which
allows us to (a) evaluate performance on a large scale, and (b) apply our model
to a diverse set of languages. We show that meaningful categories comprising
hundreds of concepts and richly structured featural representations emerge
across languages. Our work illustrates the potential of recent advances in
computational modeling and large scale naturalistic datasets for cognitive
science research.
