Date of Award
Doctor of Philosophy (PhD)
James H. Martin
Wayne H. Ward
In the last few years there have been major improvements in the performance of hard nat- ural language processing tasks due to the application of artificial neural network models. These models replace complex hand-engineered systems for extracting and representing the meaning of human language with systems which learn features based on processing examples of language. In this dissertation, I present deep neural networks for semantic role labeling, and then for Abstract Meaning Representation parsing, and a novel Distributed Abstract Meaning Representation, or DAMR. I then describe a model used to create fixed vector representations of sentence meaning from DAMR. Finally, I use natural language inference to test the quality of the meaning content of these fixed vectors.
Foland, William Roger Jr., "Natural Language Understanding: Deep Learning for Abstract Meaning Representation" (2017). Computer Science Graduate Theses & Dissertations. 148.