Social and Behavioral Sciences Commons^™

Phonotactic Learning With Distributional Representations, Max A. Nelson

Doctoral Dissertations

This dissertation explores the possibility that the phonological grammar manipulates phone representations based on learned distributional class memberships rather than those based on substantive linguistic features. In doing so, this work makes three primary contributions. First, I propose three novel algorithms for learning a phonological class system from the distributional statistics of a language, all of which are based on partitioning graph representations of phone distributions. Second, I propose a new method for fitting Maximum Entropy phonotactic grammars, MaxEntGrams, which offers theoretical complexity improvements over the widely-adopted approach taken by Hayes and Wilson [2008]. Third, I present a series of …

Examining Variability In Spanish Monolingual And Bilingual Phonotactics: A Look At Sc-Clusters, Katerina A. Tetzloff

Doctoral Dissertations

Current models of generative phonology have failed to address the variability that is observed in bilingual language patterns patterns. This dissertation addresses exactly that issue by examining the perception of Spanish sC-clusters in Spanish monolinguals and English-Spanish bilinguals. Surface sC-clusters in onset position are prohibited in Spanish and are repaired by inserting a prothetic /e/ (sC $\rightarrow$ esC). English differs in that it allows sC-cluster onsets, and the structure of the sC-cluster has been shown to differ based on the sonority profile (i.e., s+stop clusters are bisyllabic, s+liquid clusters are tautosyllabic). A batch version of a Harmonic Grammar Gradual Learning …

Learning Phonology With Sequence-To-Sequence Neural Networks, Brandon Prickett

Doctoral Dissertations

This dissertation tests sequence-to-sequence neural networks to see whether they can simulate human phonological learning and generalization in a number of artificial language experiments. These experiments and simulations are organized into three chapters: one on opaque interactions, one on computational complexity in phonology, and one on reduplication. The first chapter focuses on two biases involving interactions that have been proposed in the past: a bias for transparent patterns and a bias for patterns that maximally utilize all of the processes in a language. The second chapter looks at harmony patterns of varying complexity to see whether both Formal Language Theory …

Emergent Typological Effects Of Agent-Based Learning Models In Maximum Entropy Grammar, Coral Hughto

Doctoral Dissertations

This dissertation shows how a theory of grammatical representations and a theory of learning can be combined to generate gradient typological predictions in phonology, predicting not only which patterns are expected to exist, but also their relative frequencies: patterns which are learned more easily are predicted to be more typologically frequent than those which are more difficult. In Chapter 1 I motivate and describe the specific implementation of this methodology in this dissertation. Maximum Entropy grammar (Goldwater & Johnson 2003) is combined with two agent-based learning models, the iterated and the interactive learning model, each of which mimics a type …

Effects Of Phonological Contrast On Within-Category Phonetic Variation, Ivy Hauser

Doctoral Dissertations

This dissertation investigates an often assumed hypothesis in phonetics and phonology: that there should be relatively less within-category phonetic variation in production in languages which have relatively more phonological contrasts (Lindblom, 1986, on vowels). Although this hypothesis is intuitive, there is little existing evidence to support the claim and it is difficult to generalize outside of vowels. In this dissertation, I argue that this hypothesis is not trivially true and needs additional specification. I propose an extension of this hypothesis, Contrast-Dependent Variation, which predicts relative differences in extent of within-category variation between languages and individual speakers. Contrast-Dependent Variation can make …

Extending Hidden Structure Learning: Features, Opacity, And Exceptions, Aleksei I. Nazarov

Doctoral Dissertations

This dissertation explores new perspectives in phonological hidden structure learning (inferring structure not present in the speech signal that is necessary for phonological analysis; Tesar 1998, Jarosz 2013a, Boersma and Pater 2016), and extends this type of learning towards the domain of phonological features, towards derivations in Stratal OT (Bermúdez-Otero 1999), and towards exceptionality indices in probabilistic OT. Two more specific themes also come out: the possibility of inducing instead of pre-specifying the space of possible hidden structures, and the importance of cues in the data for triggering the use of hidden structure. In chapters 2 and 4, phonological features …

The Representation Of Probabilistic Phonological Patterns: Neurological, Behavioral, And Computational Evidence From The English Stress System, Claire Moore-Cantwell

Doctoral Dissertations

This dissertation investigates the cognitive mechanism underlying language users' ability to generalize probabilistic phonological patterns in their lexicon to novel words. Specifically, do speakers represent probabilistic patterns using abstract grammatical constraints? If so, this system of constraints would, like categorical phonological generalizations (a) be limited in the space of possible generalizations it can represent, and (b) apply to known and novel words alike without reference to specific known words. I examine these two predictions, comparing them to the predictions of alternative models. Analogical models are specifically considered. In chapter 3 I examine speakers' productions of novel words without near lexical …

Phonologically Conditioned Allomorphy And Ur Constraints, Brian W. Smith

Doctoral Dissertations

This dissertation provides a new model of the phonology-morphology interface, focusing on Phonologically Conditioned Allomorphy (PCA). In this model, UR selection occurs during the phonological component, and mappings between meanings and URs are encoded as violable constraints, called UR constraints (Boersma 2001; Pater et al. 2012). Ranking UR constraints captures many empirical generalizations about PCA, such as similarities between PCA and phonological alternations, the existence of defaults, and the interaction of PCA and phonological repairs (epenthesis, deletion, etc.). Since PCA follows from the ranking or weighting of constraints, patterns of PCA can be learned using existing learning algorithms, and modeling …

Computational Modeling Of Learning Biases In Stress Typology, Robert D. Staubs

Doctoral Dissertations

This dissertation demonstrates a strong connection between the frequency of stress patterns and their relative learnability under a wide class of learning algorithms. These frequency results follow from hypotheses about the learner's available representations and the distribution of input data. Such hypotheses are combined with a model of learning to derive distinctions between classes of stress patterns, addressing frequency biases not modeled by traditional generative theory. I present a series of results for error-driven learners of constraint-based grammars. These results are shown both for single learners and learners in an iterated learning model. First, I show that with general n …

Stress In Harmonic Serialism, Kathryn Ringler Pruitt

Open Access Dissertations

This dissertation proposes a model of word stress in a derivational version of Optimality Theory (OT) called Harmonic Serialism (HS; Prince and Smolensky 1993/2004, McCarthy 2000, 2006, 2010a). In this model, the metrical structure of a word is derived through a series of optimizations in which the 'best' metrical foot is chosen according to a ranking of violable constraints. Like OT, HS models cross-linguistic typology under the assumption that every constraint ranking should correspond to an attested language.

Chapter 2 provides an argument for modeling stress typology in HS by showing that the serial model correctly rules out stress patterns …