Naveen Khetarpal

TL;DR: This work argues for a third view based on a proposal by Jameson and D'Andrade that color naming across languages reflects optimal or near-optimal divisions of an irregularly shaped perceptual color space, and test it against color-naming data from a broad range of languages and show that it accounts for universal tendencies in color naming while also accommodating some observed cross-language variation.

TL;DR: This paper showed that a Bayesian model can learn a standard poverty-of-stimulus example, anaphoric one, from realistic input by relying on indirect evidence, without a linguistic constraint assumed to be necessary.

TL;DR: This article found that the color-naming systems of two languages that have been held to counterexemplify universals of color naming (Piraha and Warlpiri) are in fact consistent with the structure of color space.

TL;DR: It is demonstrated that this algorithm produces sensible semantic maps from two existing bodies of data and concludes that universal semantic graph structure can be automatically approximated from cross-language semantic data.

TL;DR: Khetarpal et al. as discussed by the authors showed that the spatial terms of 9 diverse languages near-optimally partition a similarity space of spatial meanings, just as color terms near-optimal partition color space.