Word learning as Bayesian inference
Joshua B. Tenenbaum, Fei Xu · eScholarship (California Digital Library) · 2000
We apply a computational theory of concept learning based on Bayesian inference (Tenenbaum, 1999) to the problem of learning words from examples. The theory provides a framework for understanding how people can generalize meaningfully from just one or a few positive examples of a novel word, without assuming that words are mutually exclusive or map only onto basic-level categories. We also