Computational Phonology

Much like the term “computational linguistics”, the term “computational phonology” has come to mean different things to different people. Research grounded in a variety of methodologies and formalisms can be included in its scope. The common thread of the research that falls under this umbrella term is the use of computational methods to investigate questions of interest in phonology, primarily how to delimit the set of possible phonological patterns from the larger set of “logically possible” patterns and how those patterns are learned. Computational phonology arguably began with the foundational result that Sound Pattern of English (SPE) rules are regular relations (provided they can’t recursively apply to their own structural change), which means they can be modeled with finite-state transducers (FSTs) and that a system of ordered rules can be composed into a single FST. The significance of this result can be seen in the prominence of finite-state models both in theoretical phonology research and in more applied areas like natural language processing and human language technology. The shift in the field of phonology from rule-based grammars to constraint-based frameworks like Optimality Theory (OT) initially sparked interest in the question of how to model OT with FSTs and thereby preserve the noted restriction of phonology to the complexity level of regular. But an additional point of interest for computational work on OT stemmed from the ways in which its architecture readily lends itself to the development of learning algorithms and models, including statistical approaches that address recognized challenges such as gradient acceptability, process optionality, and the learning of underlying forms and hidden structure. Another line of research has taken on the question of to what extent phonology is not just regular, but subregular, meaning describable with proper subclasses of the regular languages and relations. The advantages of subregular modeling of phonological phenomena are argued to be stronger typological explanations, in that the computational properties that establish the subclasses as properly subregular restrict the kinds of phenomena that can be described in desirable ways. Also, these same restrictions lead directly to provably correct learning algorithms. Once again this work has made extensive use of the finite-state formalism, but it has also employed logical characterizations that more readily extend from strings to non-linear phenomena such as autosegmental representations and syllable structure.

Phonology

Phonology is the systematic study of the sounds used in language, their internal structure, and their composition into syllables, words, and phrases. Computational phonology is the application of formal and computational techniques to the representation and processing of phonological information. This chapter presents the fundamentals of phonology along with an overview of computational phonology. Fundamentals discussed include phonological features, phonemes, early generative grammar, autosegmental phonology, syllable structure, and optimality theory. Finite-state machines, attribute-value matrices, computational learning methods, and existing software toolkits round out the discussion on comptuational phonology.

Finite-State Technology

Finite-state machines—automata and transducers—are ubiquitous in natural-language processing and computational linguistics. This chapter introduces the fundamentals of finite-state automata and transducers, both probabilistic and non-probabilistic, illustrating the technology with example applications and common usage. It also covers the construction of transducers, which correspond to regular relations, and automata, which correspond to regular languages. The technologies introduced are widely employed in natural language processing, computational phonology and morphology in particular, and this is illustrated through common practical use cases.

Computational phonology today

This thematic issue almost did not happen. One of us (JH) was almost killed two days after the deadline for article submissions. As a pedestrian on a sidewalk minding his own business, he was struck by a car that ran a red light and lost control after a collision. So when we write that we are delighted to be writing this introduction, over one year later, we both really mean it.

Computational Phonology

Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology. The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.

Review article

In his recent bid for the presidency of the Association for Mathematics of Language, Makoto Kanazawa writes:*The scientific study of language is such a large and important field that it's strange that so little mathematical research is being carried out. By comparison, the latest issue of Journal of Economic Theory has 14 original articles, and all but one of them are mathematical papers in the sense of containing theorems and proofs. And the title of the journal is not ‘Journal of Mathematical Economics’, which is actually a separate journal.1 That linguistics lags behind economics is not surprising: making more money is a stronger drive for change and progress than better understanding the language faculty. But Bruce Tesar's book shows that the time might be ripe for change and progress in our field as well. Employing a formally sophisticated analytical approach (as opposed to a purely simulation-based approach), the book provides a beautiful example of the interplay between learnability and structural assumptions on the typological space. It thus shows that computational phonology has become a mature subfield of generative linguistics.

HG Has No Computational Advantages over OT: Toward a New Toolkit for Computational OT

Various authors have recently endorsed Harmonic Grammar (HG) as a replacement for Optimality Theory (OT). One argument for this move is that OT seems not to have close correspondents within machine learning while HG allows methods and results from machine learning to be imported into computational phonology. Here, I prove that this argument in favor of HG and against OT is wrong. In fact, I show that any algorithm for HG can be turned into an algorithm for OT. Hence, HG has no computational advantages over OT. This result allows tools from machine learning to be systematically adapted to OT. As an illustration of this new toolkit for computational OT, I prove convergence for a slight variant of Boersma’s (1998) (nonstochastic) Gradual Learning Algorithm.

Phonology

This article presents the fundamentals of descriptive phonology and gives an overview of computational phonology. Phonology is the systematic study of sounds used in language, and their composition into syllables, words, and phrases. It introduces some of the key concepts of phonology by simple examples involving real data and gives a brief discussion of early generative phonology. It analyses the autosegmental phonology using some data from African tone language. This article considers in detail one level of phonological hierarchy, namely, the syllable. It reveals many interesting issues that are confronted by phonological analysis. Some of these theoretical frameworks include: lexical phonology, underspecification phonology, government phonology, declarative phonology, and optimality theory. The article provides a means for phonological generalizations such as rules and constraints to give a finite-state interpretation.