INFORMATION AND MATHEMATICAL STRUCTURES CONTAINED IN THE NATURAL LANGUAGE WORD DOMAINS AND THEIR APPLICATIONS

Nguyen Cat Ho

doi:10.15625/1813-9663/37/3/16106

Author affiliations

Authors

Nguyen Cat Ho Institute of Theoretical and Applied Research, Duy Tan University, 01 Phung Chi Kien Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/1813-9663/37/3/16106

Keywords:

Fuzzy sets, hedge algebras, linguistic rule bases, fuzzy control, fuzzy data classification, linguistic data summarization, interpretability fuzzy representation, word domains structures

Abstract

The study stands on the standpoint that there exist relationships between real-world structures and their provided information in reality. Such relationships are essential because the natural language plays a specifically vital and crucial role in, e.g., capturing, conveying information, and accumulating knowledge containing useful high-level information. Consequently, it must contain certain semantics structures, including linguistic (L-) variables’ semantic structures, which are fundamental, similar to the math variables’ structures. In this context, the fact that the (L-) variables’ word domains can be formalized as algebraic semantics-based structures in an axiomatic manner, called hedge algebras (HAs,) is still a novel event and essential for developing computational methods to simulate the human capabilities in problem-solving based on the so-called natural language-based formalism. Hedge algebras were founded in 1990. Since then, HA-formalism has been significantly developed and applied to solve several application problems in many distinct fields, such as fuzzy control, data classification and regression, robotics, L-time series forecasting, and L-data summarization. The study gives a survey to summarize specific distinguishing fundamental features of HA-formalism, its applicability in problem-solving, and its performance.

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References

General Methodology

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Cybernetics

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Classification and Regression

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Linguistic Summarization

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Image Processing

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Decision Making

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Fuzzy Time Series

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Networking

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Robotics

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