The Gradual Learning Algorithm (Boersma 1997) is a constraint ranking algorithm for learning Optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and Smolensky (1993, 1996, 1998, 2000), which initiated the learnability research program for Optimality Theory. We argue that the Gradual Learning Algorithm has a number of special advantages: it can learn free variation, deal effectively with noisy learning data, and account for gradient wellformedness judgments. The case studies we examine involve Ilokano reduplication and metathesis, Finnish genitive plurals, and the distribution of English light and dark /l/.

The study of phonotactics (e.g., the ability of English speakers to distinguish possible words like blick from impossible words like *bnick) is a central topic in phonology. We propose a theory of phonotactic grammars and a learning algorithm that constructs such grammars from positive evidence. Our grammars consist of constraints that are assigned numerical weights according to the principle of maximum entropy. Possible words are assessed by these grammars based on the weighted sum of their constraint violations. The learning algorithm yields grammars that can capture both categorical and gradient phonotactic patterns. The algorithm is not provided with any constraints in advance, but uses its own resources to form constraints and weight them. A baseline model, in which Universal Grammar is reduced to a feature set and an SPE-style constraint format, suffices to learn many phonotactic phenomena. In order to learn nonlocal phenomena such as stress and vowel harmony, it is necessary to augment the model with autosegmental tiers and metrical grids. Our results thus offer novel, learning-theoretic support for such representations. We apply the model to English syllable onsets, Shona vowel harmony, quantity-insensitive stress typology, and the full phonotactics of Wargamay, showing that the learned grammars capture the distributional generalizations of these languages and accurately predict the findings of a phonotactic experiment.

When I met Ed Stabler I was electrified by the types questions that he was asking of linguistic theories and even more so by the fact that he seemed to have an idea of how to answer those questions. Without his insight and generous assistance this dissertation would not have been written. I have also been extremely lucky to have Colin Wilson and Kie Zuraw on my committee. Colin has been an outstanding sounding board for ideas and has an uncanny ability to crash brittle models and find holes in theories. Kie’s superlative expository advice and willingness to work through the details of the most gory proofs have made this work much better and definitely clearer. My external member, Chuck Taylor, was also a great asset in encouraging me to take perspectives on linguistic issues that I otherwise wouldn’t have considered. UCLA has been a great place to work on a PhD. The community of students and faculty is always stimulating and supportive. Among the faculty that I owe the most thanks for making my time here rewarding and fun are: Bruce Hayes, for his numerous insightful comments on my work, both computational and otherwise; Pamela Munro, for being a friend and mentor and for showing me that languages aren’t quite as tidy as you might

Abstract not found

One of the many challenges to be faced in explaining language learning is the interdependence of the phonological mapping and the phonological underlying forms for morphemes (Albright and Hayes 2002; Hale and Reiss 1997; Tesar and Smolensky 2000; Tesar et al. 2003). The learner must attempt to infer both simultaneously, based on the surface forms of a language.

In this chapter we present the initial stages of work that attempts to assess the ‘psychological reality ’ of one of the more subtle grammatical principles of Optimality Theory (‘OT’; Prince and Smolensky 1993), Richness of the Base. Within the OT competence theory, we develop several of this principle’s empirical predictions concerning the grammar’s final state (Section 1) and initial state (Section 2). We also formulate linking hypotheses which allow these predictions concerning competence to yield predictions addressing performance. We then report and discuss the results of experimental work testing these performance predictions with respect to linguistic processing in infants (Section 3) and adults (Section 4). 1.

We present an unsupervised, cross-situational Bayesian learning model for the acquisition of compositional semantics. We show that the model acquires the correct grammar for a toy version of English using a psychologically-plausible amount of data, over a wide range of possible learning environments. By assuming that speakers typically produce sentences which are true in the world, the model learns the semantic representation of content and function words, using only positive evidence in the form of sentences and world contexts. We argue that the model can adequately solve both the problem of referential uncertainty and the subset problem in this domain, and show that the model makes mistakes analogous to those made by children. Keywords: Compositional semantics; language

Preprint version; to appear in Phonology, vol. 23, no. 1 In Hungarian, stems ending in a back vowel plus one or more neutral vowels show unusual behavior: for such stems, the otherwise-general process of vowel harmony is lexically idiosyncratic. Particular stems can take front suffixes, take back suffixes, or vacillate. Yet at a statistical level, the patterning among these stems is lawful: in the aggregate, they obey principles that relate the propensity to take back or front harmony to the height of the rightmost vowel and to the number of neutral vowels. We argue that this patterned statistical variation in the Hungarian lexicon is internalized by native speakers. Our evidence is that they replicate the pattern when they are asked to apply harmony to novel stems in a “wug ” test (Berko 1958). Our test results match quantitative data about the Hungarian lexicon, gathered with an automated Web search. We model the speakers’ knowledge and intuitions with a grammar based on the dual listing/generation model of Zuraw (2000), then show how the constraint rankings of this grammar can be learned by algorithm. *

Two intertwined tasks that face a learner are learning a lexicon and learning a constraint ranking. Given a set of surface forms, it is not possible in general to determine what ranking produces the given forms without knowing what the underlying forms are and similarly the underlying forms are not discernable without information about the ranking

Two questions regarding the non-local nature of long-distance agreement in consonantal harmony patterns (Hansson 2001, Rose and Walker 2004) are addressed: (1) How can such patterns be learned from surface forms alone? (2) How can we understand a a major feature of the typology—the absence of blocking effects? It is shown that a learner which generalizes only by making distinctions with respect to the order of sounds (and by not making distinctions with respect to the distance between sounds) is able to learn major classes of long-distance phonotactic patterns, and is unable to learn hypothetical long-distance phonotactic patterns with blocking effects. Thus not only is the learner able to acquire attested patterns, it explains the absence of unattested ones. Furthermore, this result lends support to the idea that long distance phonotactic patterns are phenomonologically distinct from spreading patterns contra the hypothesis of Strict Locality (Gafos 1999, et seq).