عنوان

A Multilinear Approach to the Unsupervised Learning of Morphology

Number

TLpq2281090581

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

A Multilinear Approach to the Unsupervised Learning of Morphology

General Material Designation

[Thesis]

First Statement of Responsibility

Meyer, Anthony

Subsequent Statement of Responsibility

Dickinson, Markus

Name of Publisher, Distributor, etc.

Indiana University

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

187

Dissertation or thesis details and type of degree

Ph.D.

Body granting the degree

Indiana University

Text preceding or following the note

2019

Text of Note

This dissertation presents a multilinear approach to the unsupervised learning of morphology (ULM), where multilinear refers to a multi-tiered architecture that allows for the handling of both concatenative and nonconcatenative phenomena in a general, unified way, as in autosegmental morphology. This dissertation reformulates autosegmental theory in graph-theoretic terms. That is, it identifies the essential properties that make autosegmental theory so conducive to modeling nonconcatenative morphology and shows that these properties are equivalent to the mathematical properties of a bipartite graph. This observation makes it possible to recast the autosegmental formalism as a graphical machine-learning model, namely the Multiple Cause Mixture Model (MCMM), a bipartite graphical model related to the Restricted Boltzmann Machine. The dissertation's experimental component consists of the development and evaluation of Multimorph, an MCMM-driven ULM system. The evaluation method takes a "dual-paradigm" approach, comprising both intrinsic and extrinsic components. The latter evaluates the system as a component of a larger chain of processes. This in line with lexeme-based theories of morphology, i.e., theories that regard morphology as a distinct but mediating layer of linguistic organization situated between phonology and syntax/semantics. The results of the experiments demonstrate the soundness and promise of a multilinear approach to the unsupervised learning of morphology.

Computer science

Linguistics

Dickinson, Markus

Meyer, Anthony

Electronic name

p

[Thesis]

276903

a

Y