عنوان

An Algorithm for Learning Phonological Classes from Distributional Similarity

Number

TL5jp6q2xn

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

An Algorithm for Learning Phonological Classes from Distributional Similarity

General Material Designation

[Thesis]

First Statement of Responsibility

Mayer, Connor

Subsequent Statement of Responsibility

Hayes, Bruce

Name of Publisher, Distributor, etc.

UCLA

Date of Publication, Distribution, etc.

2018

Body granting the degree

UCLA

Text preceding or following the note

2018

Text of Note

An outstanding 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. It retrieves the expected classes, and performs 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. Further refined models may provide additional insight into which phonological classes are apparent in the distributions of sounds in natural languages.

Mayer, Connor

Hayes, Bruce

UCLA

Electronic name

p

[Thesis]

276903

a

Y