This thesis addresses questions about early lexical acquisition. Four case studies provide concrete examples of how Bayesian computational modelling can be used to study assumptions about inductive biases, properties of the input data and possible limitations of the learning algorithm.
Table of Contents
1. Introduction -- 2. Background -- 3. Particle filters for word segmentation -- 4. Studying the effect of input size for Bayesian word segmentation -- 5. Exploring the role of stress in Bayesian word segmentation -- 6. A joint model of word segmentation and phonological variation -- 7. Conclusion.Notes
Theoretical thesis.
"Thesis submitted for the degree of Doctor of Philosophy, Dr. phil. at the Department of Computing, Faculty of Science, Macquarie University, Institut für Computerlinguistik, Neuphilologische Fakultät, Universität Heidelberg" -- title page.
Bibliography: pages 219-232Awarding Institution
Macquarie UniversityDegree Type
Thesis PhDDegree
PhD, Macquarie University, Faculty of Science and Engineering, Department of ComputingDepartment, Centre or School
Department of ComputingYear of Award
2014Principal Supervisor
Mark JohnsonAdditional Supervisor 1
Annette FrankRights
Copyright Benjamin Börschinger 2014.
Copyright disclaimer: http://mq.edu.au/library/copyrightLanguage
EnglishExtent
1 online resource (xvii, 232 pages) graphs, tablesFormer Identifiers
mq:71344
http://hdl.handle.net/1959.14/1273408
