Input Strictly Local opaque maps
Datos Bibliográficos
| ID | 20399643 |
|---|---|
| Autores | Jane Chandlee (0000-0001-9143-1305, Haverford College, autor de correspondencia), Jeffrey Heinz (0000-0002-5954-3195, Stony Brook University, autor de correspondencia), Adam Jardine (0000-0001-9436-8014, Rutgers Sexual and Reproductive Health and Rights, autor de correspondencia) |
| Año | 2018 |
| Volumen | 35 |
| Número | 2 |
| Páginas | 171-205 |
| Fecha de publicación | 2018-05-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Phonology (JOURNAL) |
| Identificadores de la revista | ISSN: 0952-6757 • E-ISSN: 1469-8188 |
| Editorial | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0952675718000027 |
| OpenAlex | W2806538304 |
| Idioma | EN |
| Citas recibidas | 10 |
| Referencias citadas | 44 |
This paper gives a computational characterisation of opaque interactions in phonology. Specifically, a range of opaque interactions are shown to be Input Strictly Local (ISL) maps (Chandlee 2014), which are string-to-string functions that determine output based only on contiguous sequences of input symbols. Examples from Baković’s (2007) extended typology of counterfeeding, counterbleeding, self-destructive feeding, non-gratuitous feeding and cross-derivational feeding, as well as a case of fed counterfeeding from Kavitskaya & Staroverov (2010), are all given ISL analyses, which show that these interactions can be computed based on sequences of bounded length in the input. It is discussed how ISL maps are restrictive in their typological predictions, have guaranteed learning results and known methods for generation and recognition, and thus compare favourably to rule-based and constraint-based approaches to these interactions
Bounded function · Geometry · Linguistics · Mathematical analysis · Opacity · Phonology · Physics · Computer Science · Engineering · Mathematics · Natural Language Processing Techniques · Phonetics and Phonology Research · Speech Recognition and Synthesis · Artificial Intelligence
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| Obras citantes distintas | 10 |
|---|---|
| Citas por año | 1,43 |
| Intervalo de citas | 2019 - 2026 (8) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 7 |