Tool comparison of semantic parsers
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.
Rayz, Purdue University.
Linguistics|Information Technology|Computer science
Off-Campus Purdue Users:
To access this dissertation, please log in to our