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The Nature of Subtractive Processes in Morphology  

Kazutaka Kurisu

This article discusses several important phonological issues concerning subtractive processes in morphology. First, this article addresses the scope of subtractive processes that linguistic theories should be concerned with. Many subtractive processes fall in the realm of grammatical theories. Subsequently, previous processual and affixal approaches to subtractive morphology and nonconcatenative allomorphy are reviewed. Then, theoretical restrictiveness is taken up. Proponents of the affixal view often claim that it is more restrictive than the processual view, but their argument is not convincing. We do not know enough to discuss theoretical restrictiveness. Finally, earlier analyses of subtractive morphology in parallel and serial Optimality Theory are reviewed. We have not accomplished enough in this respect, so no conclusive choice of parallelism or serialism is possible at present. As a whole, there are too many unsettled matters to conclude about the nature of subtractive processes in morphology.


Learnability and Learning Algorithms in Phonology  

Anne-Michelle Tessier

Phonological learnability deals with the formal properties of phonological languages and grammars, which are combined with algorithms that attempt to learn the language-specific aspects of those grammars. The classical learning task can be outlined as follows: Beginning at a predetermined initial state, the learner is exposed to positive evidence of legal strings and structures from the target language, and its goal is to reach a predetermined end state, where the grammar will produce or accept all and only the target language’s strings and structures. In addition, a phonological learner must also acquire a set of language-specific representations for morphemes, words and so on—and in many cases, the grammar and the representations must be acquired at the same time. Phonological learnability research seeks to determine how the architecture of the grammar, and the workings of an associated learning algorithm, influence success in completing this learning task, i.e., in reaching the end-state grammar. One basic question is about convergence: Is the learning algorithm guaranteed to converge on an end-state grammar, or will it never stabilize? Is there a class of initial states, or a kind of learning data (evidence), which can prevent a learner from converging? Next is the question of success: Assuming the algorithm will reach an end state, will it match the target? In particular, will the learner ever acquire a grammar that deems grammatical a superset of the target language’s legal outputs? How can the learner avoid such superset end-state traps? Are learning biases advantageous or even crucial to success? In assessing phonological learnability, the analysist also has many differences between potential learning algorithms to consider. At the core of any algorithm is its update rule, meaning its method(s) of changing the current grammar on the basis of evidence. Other key aspects of an algorithm include how it is triggered to learn, how it processes and/or stores the errors that it makes, and how it responds to noise or variability in the learning data. Ultimately, the choice of algorithm is also tied to the type of phonological grammar being learned, i.e., whether the generalizations being learned are couched within rules, features, parameters, constraints, rankings, and/or weightings.


Corpus Phonology  

Jacques Durand

Corpus Phonology is an approach to phonology that places corpora at the center of phonological research. Some practitioners of corpus phonology see corpora as the only object of investigation; others use corpora alongside other available techniques (for instance, intuitions, psycholinguistic and neurolinguistic experimentation, laboratory phonology, the study of the acquisition of phonology or of language pathology, etc.). Whatever version of corpus phonology one advocates, corpora have become part and parcel of the modern research environment, and their construction and exploitation has been modified by the multidisciplinary advances made within various fields. Indeed, for the study of spoken usage, the term ‘corpus’ should nowadays only be applied to bodies of data meeting certain technical requirements, even if corpora of spoken usage are by no means new and coincide with the birth of recording techniques. It is therefore essential to understand what criteria must be met by a modern corpus (quality of recordings, diversity of speech situations, ethical guidelines, time-alignment with transcriptions and annotations, etc.) and what tools are available to researchers. Once these requirements are met, the way is open to varying and possibly conflicting uses of spoken corpora by phonological practitioners. A traditional stance in theoretical phonology sees the data as a degenerate version of a more abstract underlying system, but more and more researchers within various frameworks (e.g., usage-based approaches, exemplar models, stochastic Optimality Theory, sociophonetics) are constructing models that tightly bind phonological competence to language use, rely heavily on quantitative information, and attempt to account for intra-speaker and inter-speaker variation. This renders corpora essential to phonological research and not a mere adjunct to the phonological description of the languages of the world.