scholarly journals Computational Phonology

2018 ◽  
pp. 323-341
Keyword(s):  
Computational Phonology

Finite-State Technology

2018 ◽  
Author(s):  
Mans Hulden
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Finite State Machines ◽  
Regular Languages ◽  
Finite State Automata ◽  
State Machines ◽  
Computational Phonology ◽  
Finite State

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.

Review article

Phonology ◽  
2014 ◽  
Vol 31 (3) ◽  
pp. 525-556 ◽  
Author(s):  
Giorgio Magri
Keyword(s):  
Analytical Approach ◽  
Review Article ◽  
Mathematical Research ◽  
Language Faculty ◽  
Mathematical Economics ◽  
Generative Linguistics ◽  
Computational Phonology ◽  
Simulation Based ◽  
Important Field ◽  
Mathematics Of Language

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.

Phonology

2018 ◽  
Author(s):  
Steven Bird ◽  
Jeffrey Heinz
Keyword(s):  
Optimality Theory ◽  
Generative Grammar ◽  
Syllable Structure ◽  
Computational Techniques ◽  
Computational Learning ◽  
Phonological Information ◽  
Computational Phonology ◽  
Autosegmental Phonology ◽  
Phonological Features ◽  
Finite State

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.

scholarly journals Computational Phonology

Linguistics ◽  
2019 ◽  
Author(s):  
Jane Chandlee
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Optimality Theory ◽  
Learning Algorithms ◽  
Point Of Interest ◽  
Computational Phonology ◽  
Language Technology ◽  
Finite State ◽  
State Models ◽  
Finite State Models

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.

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