Timothy J. O'Donnell

TL;DR: Analysis of a natural history of song shows that music appears in every society observed; that variation in song events is well characterized by three dimensions; that musical behavior varies more within societies than across them on these dimensions; and that music is regularly associated with behavioral contexts such as infant care, healing, dance, and love.

TL;DR: It is shown that the model is competitive with state-of-the-art spoken term discovery systems, and analyses exploring the model’s behavior and the kinds of linguistic structures it learns are presented.

TL;DR: Although exposure to counting is important to learning number word meanings, hearing number words used outside of these routines—in the quantificational structures of language—may also be highly important in early acquisition.

TL;DR: This model treats productivity and reuse as the target of inference in a probabilistic framework, asking how an optimal agent can make use of the distribution of forms in the linguistic input to learn the distributionof productive word-formation processes and reusable units in a given language.

TL;DR: This thesis presents a formal model of productivity and reuse which treats the problem as a structure-by-structure inference in a Bayesian framework and is built around two proposals: that anything that can be computed can be stored and that any stored item can include subparts which must be computed productively.