Date of Award

8-2016

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer and Information Technology

First Advisor

Julia MT Rayz

Committee Chair

Julia MT Rayz

Committee Member 1

Victor Raskin

Committee Member 2

John A. Springer

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.

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