Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 1. Categorization

Дискурс 5 (4):102-114 (2019)
  Copy   BIBTEX

Abstract

Introduction. The article opens a series of publications on the linguistics of relations (hereinafter R–linguistics), the purpose of which is to formalize the processes studied by linguistics, to expand the possibilities of their use in artificial intelligence systems. At the heart of R-linguistics is the hypothesis that mental and linguistic activity is based on the use of consciousness model of the world, which is a system of specially processed relationships observed in the world or received by consciousness in the process of communication.Methodology and sources. This article is devoted to the axiomatization of the categorization process. The research methods consist of the development of necessary mathematical concepts for linguistics.Results and discussion. Axioms of categorization are defined and their equivalence with other systems of axioms is established. The concept of linguistic spaces, which consist of categories formed on the basis of axioms, is formulated. The properties of linguistic spaces are defined. In the paper are introduced the concepts of forming species which are important in decompositions of spaces, and in the transition to a parametric representation and language. Three variants of categorization are considered, the most important of which is verbal categorization. The evaluation of the results and their further development in different directions is carried out.Conclusion. At the end of the article some additional comments are made for further publications of the series.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 101,174

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Dual PECCS: A Cognitive System for Conceptual Representation and Categorization.Antonio Lieto, Daniele Radicioni & Valentina Rho - 2017 - Journal of Experimental and Theoretical Artificial Intelligence 29 (2):433-452.

Analytics

Added to PP
2020-01-12

Downloads
10 (#1,472,500)

6 months
9 (#492,507)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references