Problems of extracting semi-structured textual information based on Text Mining technology (using the material of the Russian and Chuvash languages)
Gubanov Aleksey Rafailovich, Danilov Andrey Anatolyevich, Isaev Yuri Nikolaevich, Gubanova Galina Fedorovna
Chuvash State University
Chuvash State Institute of Humanities
Submitted: 10.07.2024
Abstract. The study aims to identify models and algorithms for processing textual information related to modal correction of intentional relationship schemes in languages with different structures based on Text Mining technology. The growth of diverse textual information flows on the internet, consisting of complexly organized documents, poses challenges for analysts. Such challenges are related to differentiated knowledge extraction (Text Mining technology is used in the analysis of diverse textual information). The paper proposes an approach to analyzing information related to modal correction of schemes of intentional semantic relations in languages with different structures involving methods of computational linguistics and Text Mining. Using the Language Resources library, an analysis of Russian and Chuvash corpora in the Datastores database was conducted (transferring information based on an analysis of the problems of integration and compatibility of data with various types of documents from different sources). Based on the proposed conceptual approach, clustering is performed (of document clusters, of the text corpus). The scientific novelty of the study lies in developing a complex of models and algorithms for analyzing intentional relations in languages with different structures, in particular, in Russian and Chuvash, ensuring accuracy and completeness in extracting information in search queries. Attention is focused on the content of linguistic resources; a classification of linguistic resources is conducted according to class-modes of intentional semantic relations. An approach to formalizing lexico-syntactic templates is determined, and on their basis, the task of constructing a taxonomy for the concept of intentional semantic relations is solved. As a result of the study, it has been found that the proposed method is effective for solving problems of Text Mining and interpreting its results.
Key words and phrases: искусственный интеллект, разноструктурные языки, интенциональные смысловые отношения (ИСО), Text Mining, GATE, Data Mining, artificial intelligence, languages with different structures, intentional semantic relations
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