Dependency Parsing and Dialogue Systems : an investigation of dependency parsing for commercial application
Year: 2017
Type: dissertation
Abstract: In this thesis, we investigate dependency parsing for commercial application, namely for future integration in a dialogue system. To do this, we conduct several experiments on dialogue data to assess parser performance on this domain, and to improve this performance over a baseline. This work makes the following contributions: first, the creation and manual annotation of a gold-standard data set for dialogue data; second, a thorough error analysis of the data set, comparing neural network parsing to traditional parsing methods on this domain; and finally, various domain adaptation experiments show how parsing on this data set can be improved over a baseline. We further show that dialogue data is characterized by questions in particular, and suggest a method for improving overall parsing on these constructions.
Author: Allison Adams
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2887508361