An algorithm for learning phonological classes from distributional similarity

Dados Bibliográficos

ID20399367
AutoresConnor Mayer (0000-0003-2710-3475, University of California, Los Angeles, autor correspondente)
Ano2020
Volume37
Fascículo1
Páginas91-131
Data de publicação2020-02-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPhonology (JOURNAL)
Identificadores do periódicoISSN: 0952-6757 • E-ISSN: 1469-8188
EditoraCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0952675720000056
OpenAlexW2909676388
IdiomaEN
Citações recebidas5
Referências citadas56

An important question in phonology is to what degree the learner uses distributional information rather than substantive properties of speech sounds when learning phonological structure. This paper presents an algorithm that learns phonological classes from only distributional information: the contexts in which sounds occur. The input is a segmental corpus, and the output is a set of phonological classes. The algorithm is first tested on an artificial language, with both overlapping and nested classes reflected in the distribution, and retrieves the expected classes, performing well as distributional noise is added. It is then tested on four natural languages. It distinguishes between consonants and vowels in all cases, and finds more detailed, language-specific structure. These results improve on past approaches, and are encouraging, given the paucity of the input. More refined models may provide additional insight into which phonological classes are apparent from the distributions of sounds in natural languages

Linguistics · Natural language · Natural language processing · Phonological rule · Phonology · Speech recognition · Computer Science · Music and Audio Processing · Phonetics and Phonology Research · Speech Recognition and Synthesis · Artificial Intelligence