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  • СЕМАНТИЧЕСКАЯ ОБРАБОТКА НЕСТРУКТУРИРОВАННЫХ ТЕКСТОВЫХ ДАННЫХ НА ОСНОВЕ ЛИНГВИСТИЧЕСКОГО ПРОЦЕССОРА PULLENTI
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News
September 21, 2026
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
September 21, 2026
Algebra, Geometry, and AI: Russian and Vietnamese Mathematicians Discuss Current Research
A delegation of scientists from Hanoi visited the HSE Faculty of Computer Science and then took part in a Russian-Vietnamese conference in St Petersburg. The events were part of the three-year project ‘Flexibility and Computational Methods.’ Over the course of the project, the researchers have prepared joint publications and obtained new mathematical results.
September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.

 

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СЕМАНТИЧЕСКАЯ ОБРАБОТКА НЕСТРУКТУРИРОВАННЫХ ТЕКСТОВЫХ ДАННЫХ НА ОСНОВЕ ЛИНГВИСТИЧЕСКОГО ПРОЦЕССОРА PULLENTI

Информатика и ее применения. 2018. Т. 12. № 3. С. 91–98.
Козеренко Е. Б., Кузнецов К. И., Romanov D. A.

The paper presents the method for creation of knowledge extraction systems based on the approach employing the software tool system PullEnti comprising the algorithms for morphological and semantic-syntactical analysis which makes it possible to extract entities of certain types from natural language texts (persons, organizations, locations, and other target semantic objects). The PullEnti system uses dynamically connected components (plugins) which makes it possible to activate various functions without recompiling. This is how the semantic analysis unit is incorporated. During the analysis, the semantic units (tokens) are established, which are typed phrases: text, numerical data, etc. Examples of implemented projects for different subject areas are given.

Research target: Computer Science
Priority areas: business informatics
Language: Russian
DOI
Text on another site
Keywords: семантический поисксемантическое моделированиеsemantic modelingsemantic searchинтеллектуальные технологииKnowledge Extractionименованные сущностиintelligent information systemsизвлечение знаний из текстовnamed entity recognition
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