With the rapid growth of information on the internet, the accumulation of large databases, and the constant influx of data from various sensors and intelligent systems, it is becoming increasingly difficult for users to find what they are really looking for. Therefore, the development of automatic summarization methods is considered a crucial task in natural language processing. These needs have motivated the development of various methods and approaches for extracting semantic and semantic information from documents, classifying it, and systematizing it. This article develops the architecture of a hybrid-syntactic fuzzy system for extracting semantic features from text and presents its mathematical formalization. The author's method enables a transition from empirical assessments of word importance to a rigorous formalized calculation of their semantic weight.

Keywords: semantics, sentence, extraction, fuzzy logic, comparison, data

The development of digital learning platforms, electronic document management systems, and web-based systems that process natural language text information has led to an increase in the volume of content and/or arrays of processed full-text documents. This, in turn, has increased the demand for highly effective natural language processing methods capable of capturing text semantics. This article proposes a hybrid architecture based on the integration of probabilistic and fuzzy logic that effectively addresses semantic ambiguity issues by integrating stochastic and fuzzy logic channels that take into account both statistical patterns and linguistic uncertainty.

Keywords: integration, semantics, interpretation, natural language, uncertainty, patterns