Methods and techniques for cognitive adaptation of specialized socio-economic texts for target audiences: review and prospects for the development of information systems

Abstract

The article discusses the problem of cognitive adaptation of specialised socio-economic texts for the target audience. To solve the problem, an interdisciplinary approach was chosen at the intersection of cognitive-discursive linguistics, terminology, information technology, computational linguistics, and natural language processing. We provide a systematic description of methods for automating the process of text adaptation and creating complex information systems that generate secondary texts optimised for perception by a specific group of recipients. Particular attention is paid to issues of cognitive load and conceptual density of the text, as well as the semantic invariance of the secondary text. We compare methods and justify their effectiveness for different stages of designing an information system that automates this process. We examine tools for statistical assessment of text complexity, target audience classification, general text simplification, and terminology adaptation, implemented in the Python programming language. As an illustration of the proposed solution, an example is given of the semi-automatic (using a neural network with subsequent expert correction) conversion of a fragment of a specialized socio-economic text from a scientific article. The results propose a flowchart of the prototype of the information system under development. The presented study can serve as a basis for the development of an information system that can be useful to state and municipal authorities for improving communication with citizens, to the media for increasing the readability of publications and audience engagement, to educational institutions for teaching complex socio-economic disciplines, businesses to explain the financial, legal, and social aspects of their activities, etc. The proposed approach is universal and can be scaled to other subject areas.