AI-Based Algorithm for Significant Information Extraction from Unstructured Textual Data

With the growing volume of unstructured information worldwide, it has become essential to develop efficient methods for data analysis and systematization. This paper presents an algorithm for processing unstructured textual data using artificial neural networks (ANNs) to automate the extraction of key information. The proposed algorithm employs neural networks as its core tool for locating and processing relevant data. It also integrates computer vision libraries to convert file contents into a textual format, which is then fed into the neural network. The processed results are returned as a JSON array, simplifying further data handling. A distinctive feature of the algorithm is its ability to extract target information from files with variable structures without relying on predefined templates. This work has practical implications for researchers seeking to optimize scientific information retrieval and analysis, as well as for developers working in natural language processing (NLP). Automating data extraction significantly reduces the time required for manual text document processing.