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This paper is devoted to the study of methods for information extraction(entity recognition and relation classification) from scientific texts oninformation technology. Scientific publications provide valuable informationinto cutting-edge scientific advances, but efficient processing of increasingamounts of data is a time-consuming task. In this paper, several modificationsof methods for the Russian language are proposed. It also includes the resultsof experiments comparing a keyword extraction method, vocabulary method, andsome methods based on neural networks. Text collections for these tasks existfor the English language and are actively used by the scientific community, butat present, such datasets in Russian are not publicly available. In this paper,we present a corpus of scientific texts in Russian, RuSERRC. This datasetconsists of 1600 unlabeled documents and 80 labeled with entities and semanticrelations (6 relation types were considered). The dataset and models areavailable at https://github.com/iis-research-team. We hope they can be usefulfor research purposes and development of information extraction systems.

Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian