STRUCTURING INFORMATION ECOSYSTEM VIA LINGUISTIC INDEXATION

Abstract

The article deals with the role of linguistic indexation in arranging the modern information ecosystem in the era of digital content and the ever-expanding use of media texts. Shift from manual to automated information retrieval highlighted the necessity of intelligent search technologies capable of efficiently processing large volumes of unstructured textual data. This paper outlines both the theoretical and practical ground of linguistic indexation, namely term indexing, metonymic indexing and syntactic indexing. The study also views linguistic indexation within broader developments in computational linguistics, data mining and artificial intelligence, showing how these tools improve search outcomes by enabling semantic understanding and contextual awareness. A detailed analysis is presented on how indexing systems generate search images through keywords and descriptors, facilitating both clarity and reliability in content discovery. Despite notable advances in the digitalized world, the authors stress that existing intelligent search systems often fall short in working with the specialized or domain-specific content, where nuanced linguistic features may not be subjected to generic indexing mechanisms. Consequently, the article strives for ongoing innovation in algorithmic development, indexing strategies and software tools tailored to specific sectors. The article concludes that linguistic indexation serves as a foundational mechanism for structuring the information ecosystem, offering substantial improvements in speed, accuracy and user relevance of digital search environments. In light of the continual expansion of digital text, the authors underline the need for sustained interdisciplinary research to optimize linguistic processing tools and ensure they remain adaptive to the evolving demands of information retrieval and content analysis.