In this thesis, recent computational models of phonotactics are discussed and evaluated and two new models are implemented. Prior phonotactic modeling, motivated by gradient acceptability judgments in nonce word judgment tasks (Albright 2009), claim that phonotactic grammaticality is gradient, and these models are evaluated by their ability to judge nonce words with scores that correlate with human acceptability judgments. Gorman (2013) argues that these gradient models do not account for the facts sufficiently and claims phonotactic grammaticality is categorical. In this thesis, the account of Gorman (2013) is implemented as well as a prominent gradient model from Hayes and Wilson (2008) and compared with the performance of two machine learning models (a support vector machine and a recurrent neural network), with all models trained on a corpus of English onsets. Results in this thesis show that the computational models are unable to correlate with human judgment data from Scholes (1966) as well as a categorical prediction of acceptability based on whether a sequence is attested in the lexicon or not, and that these models rely on assumptions which when challenged show that the models do not convincingly capture the gradience of the human judgment data used for evaluation.
Read
