Dissertation - Michael Becker

speakers partition the lexicon by the size of its nouns, and then project the lexical statisticsonto novel items.In the proposed model, the language learner identified the most specific lexical trendthat can be expressed with constraint interaction. Whenever the behavior of lexical itemscauses ranking conflicts, lexical items are recorded with reference to two most specificconflicting constraints: One of the constraints is cloned, and items are listed under a clone,paired with the other constraint that was involved in the conflict. In Turkish, this allowsspeakers to combine the place effect and the size effect, listing nouns according to theirsize and the place of their final stop.Since the model only uses the Universal constraints in CON to record lexical trends, itignores facts about the lexicon that cannot be expressed with universal constraints. Sincelanguages are not observed to have interactions of obstruent voicing with the height orbackness of neighboring vowels, there are no constraints that penalize combinations ofvoicing with neighboring vowel qualities.In the absence of such constraints, Turkishspeakers cannot record the effect that vowel height and backness have on the distributionof voicing alternations.2.5 General-purpose learning with the MGLThe Minimal Generalization Learner (MGL) of Albright & Hayes (2002, 2003, 2006)is an information-theoretic algorithm that generalizes patterns over classes of words thatundergo similar alternations. MGL provides a reflection of trends in the lexicon and hasthe potential to generalize them to novel outputs. The MGL has been shown to successfullymodel humans’ experimental results in novel word-formation tasks with the past tense inEnglish and with similar tasks in other languages, and is thus a good representative ofa class of models that access lexical patterns without any bias against generalizing fromphonologically unnatural trends.65