Alignment of phonetic sequences is a necessary step in many applications in computational phonology. After discussing various approaches to phonetic alignment, I present a new algorithm that combines a number of techniques developed for sequence comparison with a scoring scheme for computing phonetic similarity on the basis of multivalued features. The algorithm performs better on cognate alignment, in terms of accuracy and efficiency, than other algorithms reported in the literature.

An extension to finite state transducers is presented, in which atomic symbols are replaced by arbitrary predicates over symbols. The extension is motivated by applications in natural language processing (but may be more widely applicable) as well as by the observation that transducers with predicates generally have fewer states and fewer transitions. Although the extension is fairly trivial for finite state acceptors, the introduction of predicates is more interesting for transducers. It is shown how various operations on transducers (e.g. composition) can be implemented, as well as how the transducer determinization algorithm can be generalized for predicate-augmented finite state transducers.

In this paper I present a novel Machine Learning approach to the acquisition of stochastic string transductions based on Pair Hidden Markov Models (PHMMs), a model used in computational biology. I show how these models can be used to learn morphological processes in a variety of languages, including English, German and Arabic. Previous techniques for learning morphology have been restricted to languages with essentially concatenative morphology.

The computation of the optimal phonetic alignment and the phonetic similarity between words is an important step in many applications in computational phonology, including dialectometry. After discussing several related algorithms, I present a novel approach to the problem that employs a scoring scheme for computing phonetic similarity between phonetic segments on the basis of multivalued articulatory phonetic features. The scheme incorporates the key concept of feature salience, which is necessary to properly balance the importance of various features. The new algorithm combines several techniques developed for sequence comparison: an extended set of edit operations, local and semiglobal modes of alignment, and the capability of retrieving a set of near-optimal alignments. On a set of 82 cognate pairs, it performs better than comparable algorithms reported in the literature.

Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching is one approach that can be used to issue future predictions, but it scales poorly with large data sources and is unable to make intelligent predictions given previously unseen input data, even when there is an obvious relationship between past input and the output it generated. In this paper, we describe a novel way to combine classification and transduction for a more efficient and accurate value prediction strategy, one capable of issuing predictions about previously unseen hints. We show how our approach results in significant speedups for plans that query multiple sources or sources that require multi-page navigation. 1

Alignment of phonetic sequences is a necessary step in many applications in computational phonology. In this paper, I critically evaluate several approaches to phonetic alignment that have been reported within the last few years. I then present a new algorithm that is geared towards the alignment of cognates. I show how the basic dynamic programming algorithm for sequence comparison can be modified to deal with a range of phonological phenomena. For computing phonetic similarity, I propose a scoring scheme that is based on multivalued features. Finally, I provide a complete test set that demonstrates the new algorithm performs better on cognate alignment, in terms of accuracy and efficiency, than other algorithms reported in the literature. Keywords: alignment, distance, features, phonetic, sequence Available at: http://www.cs.utoronto.ca/csri/reports.html 1 Computational Linguistics Research Group, Department of Computer Science, University of Toronto, 10 King's College ...

my first and most influential teachers. For their encouragement, understanding, and love. ii Acknowledgements I would very much like to thank my thesis advisor Craig Knoblock for the many enjoyable years of mentorship, support, and friendship. Craig has always given me the freedom to explore my own paths towards solving a problem, encouraged me to take chances, while at the same time challenging me to back up my claims and to sometimes consider alternative approaches. Through him, I learned how to read research papers as well as how to write them. His thoughts and advice greatly influenced and improved this thesis. I am extremely grateful for his guidance and I know that it will continue to inspire me as I work with and mentor others.

iii ACKNOWLEDGMENTS I am very pleased to have had the encouragement and support of a committee of three linguists for whom I have the greatest respect and admiration: Mark Liberman, William Labov and Eugene Buckley. Each of them made my transition back to Penn pleasant after what seemed like a long absence. It was a great pleasure to have Mark Randolph both as an external reader and as a colleague at Motorola. Mark’s work at MIT a decade ago has served as an inspiration to me. Orhan Karaali made this dissertation possible in this millennium. As my manager for over two years at Motorola, Orhan insisted on making my dissertation a priority at work. Harry Bliss provided his voice to this project and our whole group is very grateful for his patience and cooperation. My colleagues at Motorola listened to my ideas and provided technical and theoretical assistance at every turn: Noel

This paper presents a previously unnoticed universal property of stress patterns in the world’s languages: they are, for small neighborhoods, neighborhood-distinct. Neighborhood-distinctness is a locality condition defined in automata-theoretic terms. This universal is established by examining stress patterns contained in two typological studies, Bailey (1995) and Gordon (2002). Strikingly, many logically possible— but unattested—patterns do not have this property. Not only does neighborhood-distinctness unite the attested patterns in a non-trivial way, it also naturally provides an inductive principle allowing learners to generalise from limited data. A learning algorithm is presented which generalises by failing to distinguish same-neighborhood environments perceived in the learner’s linguistic input—hence learning neighborhood-distinct patterns—as well as almost every stress pattern in the typology. In this way, this work lends support to the idea that properties of the learner can explain certain properties of the attested typology, an idea not straightforwardly available in Optimality-theoretic and Principle and Parameter frameworks.

rvene between the target and the trigger Authors email addresses: albright@ling.ucsc.edu, bhayes@humnet.ucla.edu. ## Limited set of innate constraints (many Optimality theorists, e.g., Tesar and Smolensky 2000): learner pre-equipped with finite set of constraints like * # # ... # # . ## UG-agnostic responses: ##Assume that at least some processes really are non-local, and that the correct constraints are not handed to the learner by UG, but must be discovered by an inductive learning mechanism. ##If an inductive approach succeeds, the evidence that any constraint it learns is in UG becomes weaker; if it fails without the help of assisting principles of UG, then the evidence for those principles is strengthened (Gildea and Jurafsky 1996). 5. Our General Approach to Finding Environments as developed for the study of local environments (Albright, Andrade and Hayes 2001; Albright, in press; Albright and Hayes 2002): I. Generalize bottom-up from the lexicon