FORMALIZATION OF RECOGNITION AND IDENTIFICATION OF SEMANTIC OBJECTS IN NATURAL LANGUAGE TEXT STREAMS

The increasing incidence of crimes committed in cyberspace, particularly on social networks andvarious messengers, necessitates the development of adequate and effective countermeasures. The rise incybercrime is so significant that it poses a potential threat of inflicting irreparable harm to the state andsociety. However, detecting such crimes and criminal activities is challenging because offenders operatevirtually and linguistically within social networks, exploiting their features to conceal their traces. Nonetheless,various detection and identification tools capable of automatically processing natural language,highlighting specific semantic features of criminal activities, and recognizing and identifying them couldserve as effective countermeasures. Given the impracticality of applying neural network approaches tothese situations for several reasons, this study proposes a formal method for designing a recognizer toidentify semantic objects in text streams based on their linguistic traces. Formal concepts such as the formalmodel of a semantic object, behavior function, scenario, linguistic trace, and recognition function areintroduced. The reasoning is based on set-theoretical principles of computational theory of semantic interpretationand utilizes computational representations of the meaning of text fragments for their comparisonin terms of semantic similarity. The proposed approach is general and universal, allowing for theformal synthesis of a recognizer for semantic objects based on their linguistic descriptions and behavior.All discussions and constructions in the work are illustrated with specific examples.