Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2019

Abstract

Despite the relative successes of natural language processing in providing some useful interfaces for users, natural language understanding is a much more difficult issue. Natural language processing was one of the main topics of AI for as long as computers were put to the task of generating intelligent behavior, and a number of systems that were created since the inception of AI have also been characterized as being capable of natural language understanding. However, in the existing domain of natural language processing and understanding, a definition and consensus of what it means for a system to “truly” understand language do not exist. For a system to understand an idea, firstly it has to ground the meaning of the concepts in the idea that it manipulates - the concepts that are associated with the words it inputs and outputs. However, there has not been any standardized consensus on what constitutes adequate semantic grounding. This paper presents a spatio-temporal representational method as a basis for a specification of what constitutes adequate semantic grounding, particularly in connection with certain words and concepts related to grounding of physical concepts and mental constructs. This research has critically important implication for learning – true language understanding will usher in an era of learning through language instruction, which is how humans learn, to rapidly accumulate a vast amount of knowledge critical to the propagation of the species and the advancement of its civilization.

Keywords

Grounding of mental constructs, Grounding of physical concepts, Natural language understanding, Semantic grounding, Spatio-temporal representation

Discipline

Artificial Intelligence and Robotics | Programming Languages and Compilers

Research Areas

Intelligent Systems and Optimization

Publication

Artificial Intelligence and Security: 5th International Conference, ICAIS 2019, New York, July 26-28: Proceedings

Volume

11633

First Page

87

Last Page

99

ISBN

9783030242640

Identifier

10.1007/978-3-030-24265-7_8

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-030-24265-7_8

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