Speaker: Meaghan Fowlie (McGill)
Date & Time: Friday, November 20th at 3:30 pm
Place: ARTS Bldg. room 260
Title: Modelling and Learning Adjuncts

Adjuncts have among their properties optionality and iterability, which are usually accounted for with a grammar in which the presence or absence of an adjunct does not affect the state of the derivation. For example, in a phrase structure grammar with rules like NP -> AP NP, we have an NP whether or not we have an adjective. However, certain adjuncts like adverbs and adjectives are often quite strictly ordered, which cannot be accounted for with a model that treats a phrase the same regardless of the presence of another adjunct: whether or not a particular adjunct has adjoined affects whether or not another adjunct may adjoin. I present a minimalist model that can handle all of these properties.

In terms of learning, I cover three topics: language learning algorithms and how they handle optionality and repetition; an artificial language learning experiment about repetition, and, just for fun, the use of machine learning to analyse the song of the California Thrasher, showing that their unbounded repetition lends itself much better to a human-language-like grammar than simple transitional probabilities.