Dissertation - Michael Becker

domains of linguistic investigation: In the cross-linguistical study of regular phonologicalphenomena, and in the language-specific study of the distribution of lexically-determinedphonological processes.The MGL results are representative of a wider range of learning algorithms, such asCART (Breiman et al. 1984) or C4.5 (Quinlan 1993), which use purely distributionalproperties of a lexicon to model human behavior. The MGL’s advantage over these othermodels is that it isn’t given a list of possible generalizations to explore in advance, butrather generates its own set of hypotheses. With models other than the MGL, the lackof vowel effect could be hard-wired by not supplying the model with information aboutvowel quality. Since these models are not specific to language and therefore don’t haveany information about natural phonological interactions, such an exercise would offerlittle insight into the problem at hand. The MGL simulation is informative specificallybecause it is given whole words to deal with, without additional information about whichgeneralizations to attend to.The MGL results show that a model that isn’t equipped with a set of biases thatdetermine the universal range of phonological interactions will be unable to successfullymimic human behavior and ignore accidental regularities in a lexicon.2.6 UR-based approaches to final devoicingThe traditional generative analysis of Turkish voicing alternations (Lees 1961; Inkelas& Orgun 1995; Inkelas et al. 1997) attributes different underlying representations to wordfinalstops based on their behavior (although a different approach was suggested in Hayes1995b). There is no explicit analysis of Turkish in terms of Optimality Theory, but ananalysis in the spirit of Inkelas et al. (1997) would be something like (67). In this analysis,nouns that surface with a voiceless stop throughout the paradigm have a voiceless stopunderlyingly, while stops that alternate have an underlying stop that is unspecified for[±voice].69