Language Model Architecture Based on the Syntactic Graph of Analyzed Text

The methods and techniques of graph structures for text processing are considered. The task of processing Russian-language text and extracting semantic structures is an important stage in the development of artificial intelligence systems. Existing models of intelligent assistants are unable to handle a large volume of noisy information and take a long time to process requests. To solve this problem, the article proposes methods for working with graph structures for the analysis and classification of necessary data. By performing initial processing according to the proposed conceptual structure, it becomes possible to use a syntactic graph for a more accurate representation of each part of speech in the processed context. The results of the tested model provided data on the accuracy of word identification in Russian-language sentences. A table comparing the accuracy with existing natural language processing models is presented. The results were obtained based on the fact that 70% of the text volume is required for the training set, and analysis was conducted on the remaining portion, which is true for each of the compared model.