A System for Information Extraction from Scientific Texts in Russian

• Published in: Data Analytics and Management in Data Intensive Domains Vol. 1620; pp. 234 - 245
• Main Authors: Bruches, Elena, Mezentseva, Anastasia, Batura, Tatiana
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Entity linking; Knowledge base; Relation extraction; Term extraction; Weakly supervised learning
• ISBN: 9783031122842; 3031122844
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-031-12285-9_15

In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.