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Shaping Phonology. 音韻論の形成
・ISBN 978-0-226-56245-2 cloth US$ 64.00
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電子版あり 大学・学術機関向け電子ブック(eBook)ISBN 978-0-226-56259-9
| 著者・編者 | Brentari, Diane / Lee, J. L. (eds.), |
|---|---|
| 出版社 | (U. Chicago Pr., US) |
| 出版年月 | 2018 |
| ページ数 | 320 pp. |
| 言語 | ENG |
| ニュース番号 | <644-3412 644-P6542> |
解説
Within the past forty years, the field of phonology-a branch of linguistics that explores both the sound structures of spoken language and the analogous phonemes of sign language, as well as how these features of language are used to convey meaning-has undergone several important shifts in theory that are now part of standard practice. Drawing together contributors from a diverse array of subfields within the discipline, and honoring the pioneering work of linguist John Goldsmith, this book reflects on these shifting dynamics and their implications for future phonological work.
Divided into two parts, Shaping Phonology first explores the elaboration of abstract domains (or units of analysis) that fall under the purview of phonology. These chapters reveal the increasing multidimensionality of phonological representation through such analytical approaches as autosegmental phonology and feature geometry. The second part looks at how the advent of machine learning and computational technologies has allowed for the analysis of larger and larger phonological data sets, prompting a shift from using key examples to demonstrate that a particular generalization is universal to striving for statistical generalizations across large corpora of relevant data. Now fundamental components of the phonologist's tool kit, these two shifts have inspired a rethinking of just what it means to do linguistics.
Divided into two parts, Shaping Phonology first explores the elaboration of abstract domains (or units of analysis) that fall under the purview of phonology. These chapters reveal the increasing multidimensionality of phonological representation through such analytical approaches as autosegmental phonology and feature geometry. The second part looks at how the advent of machine learning and computational technologies has allowed for the analysis of larger and larger phonological data sets, prompting a shift from using key examples to demonstrate that a particular generalization is universal to striving for statistical generalizations across large corpora of relevant data. Now fundamental components of the phonologist's tool kit, these two shifts have inspired a rethinking of just what it means to do linguistics.