@article{TACL276,
        author = {Brian McMahan and Matthew Stone},
        title = {A Bayesian Model of Grounded Color Semantics},
        journal = {Transactions of the Association for Computational Linguistics},
        volume = {3},
        year = {2015},
        keywords = {},
        abstract = {Natural language meanings allow speakers to encode important
real-world distinctions, but corpora of grounded language use also reveal
that speakers categorize the world in different ways and describe situations
with different terminology.  To learn meanings from data, we therefore need
to link underlying representations of meaning to models of speaker judgment
and speaker choice.  This paper describes a new approach to this problem: we
model variability through uncertainty in categorization boundaries and
distributions over preferred vocabulary.  We apply the approach to a large
data set of color descriptions, where statistical evaluation documents its
accuracy.  The results are available as a Lexicon of Uncertain Color
Standards (LUX), which supports future efforts in grounded language
understanding and generation by probabilistically mapping 829 English color
descriptions to potentially context-sensitive regions in HSV color space.},
        issn = {2307-387X},
        url =
{https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/276},
        pages = {103--115}
}