Ambiguity Resolution in Language Learning: Computational and Cognitive Models

by Hinrich Schütze

This volume is concerned with how ambiguity and ambiguity resolution are learned, that is, with the acquisition of the different representations of ambiguous linguistic forms and the knowledge necessary for selecting among them in context. Schütze concentrates on how the acquisition of ambiguity is possible in principle and demonstrates that particular types of algorithms and learning architectures (such as unsupervised clustering and neural networks) can succeed at the task. Three types of lexical ambiguity are ambiguity in syntactic categorisation, semantic categorisation, and verbal subcategorisation. The volume presents three different models of ambiguity Tag Space, Word Space, and Subcat Learner, and addresses the importance of ambiguity in linguistic representation and its relevance for linguistic innateness.

176 pages, Hardcover

First published January 1, 1997

About the author

Professor Dr. Hinrich Schütze is Chair of Computational Linguistics and Director of the Center for Information and Language Processing at the University of Munich (LMU).