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Exploring issues in lexical acquisition using Bayesian modelling

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posted on 2022-03-29, 03:49 authored by Benjamin Börschinger
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.


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 219-232

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Mark Johnson

Additional Supervisor 1

Annette Frank


Copyright Benjamin Börschinger 2014. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (xvii, 232 pages) graphs, tables

Former Identifiers

mq:71344 http://hdl.handle.net/1959.14/1273408