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Ontological approach to Chinese text processing

https://doi.org/10.35596/1729-7648-2020-18-6-49-56

Abstract

To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

About the Author

Q. Longwei
Belarusian State University of Informatics and Radioelectronics
Belarus

Qian Longwei, PG Student of the Department of Intelligent Information Technologies

220037, Republic of Belarus, Minsk, Platonava str., 39

tel. +375-29-721-60-63



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Review

For citations:


Longwei Q. Ontological approach to Chinese text processing. Doklady BGUIR. 2020;18(6):49-56. (In Russ.) https://doi.org/10.35596/1729-7648-2020-18-6-49-56

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ISSN 1729-7648 (Print)
ISSN 2708-0382 (Online)