Abstract
Natural Language Processing (NLP) is a vital aspect for artificial intelligence systems to achieve integration into human lives, which has been a goal for researchers in this industry. While NLP focuses on an array of problems, semantic parsing will be specifically focused on throughout this paper. These parsers have been considerably targeted for improvement through the scientific community and demand for semantic parsers that achieve high accuracy has increased. There have been many approaches developed for this specific purpose and in this paper, a deep analysis was performed to compare the performance of semantic parsing systems. The implications of this comparison provides a viewpoint of how semantic parsers from different eras compare on a set of shared metrics.
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer and Information Technology
Committee Chair
Julia MT Rayz
Date of Award
8-2016
Recommended Citation
Hinh, Robert, "Tool comparison of semantic parsers" (2016). Open Access Theses. 972.
https://docs.lib.purdue.edu/open_access_theses/972
First Advisor
Julia MT Rayz
Committee Member 1
Victor Raskin
Committee Member 2
John A. Springer