Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 4. Language
Introduction. The paper continues a series of publications on linguistics of relations (hereinafter R-linguistics) and is devoted to questions of the formation of a language from a linguistic model of the world. Moreover, the language is considered in its most general form, without taking into account the grammatical component. This allows you to focus on the general problems of language formation. Namely, this allows us to show why language adequately reflects the model of the world and what
... e the features of the transition from model to language. This new approach to language is relevant in connection with the formation of an understanding of the common core in all natural languages, as well as in connection with the needs for the formation of artificial intelligence subsystems of interaction with humans. Methodology and sources. Research methods consist in the formulation and proof of theorems about language spaces and their properties. The materials of the paper and the given proofs are based on the previously stated ideas about linguistic spaces and their decompositions into signs. Results and discussion. The paper shows how, in the most general form, the formation of language structures takes place. Namely, why does language adequately reflect the linguistic model, and what is the difference between linguistic and language spaces? The concepts of an open and closed form of the language are formulated, as well as the law of form. Examples of open and closed forms of the language are shown. It is shown that the formation of the language allows you to compensate for the lack of real signs in the surrounding world while maintaining the prognostic properties of the model. Conclusion. Any natural language is a reflection of the human world model. Moreover, all natural languages are similar in terms of the principles of forming the core of the language (language space). Language spaces standardize the models of the world by equalizing real and fictional signs of categories. In addition, the transition to language simplifies some of the problems of pattern recognition and opens the way to the logic of natural language.