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Разработка автоматизированных методов порождения служебных документов на естественном языке

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dc.contributor.author Пальчунов, Дмитрий Евгеньевич ru_RU
dc.contributor.author Финк, Артём Альбертович ru_RU
dc.contributor.author Palchunov, D. E. en
dc.contributor.author Fink, A. A. en
dc.creator Новосибирский государственный университет ru-Ru
dc.creator Институт математики им. С. Л. Соболева СО РАН ru-Ru
dc.creator Novosibirsk State University en
dc.creator Institute of Mathematics SB RAS en
dc.date.accessioned 2017-10-02T13:17:34Z
dc.date.available 2017-10-02T13:17:34Z
dc.date.issued 2017-09
dc.identifier.citation Пальчунов Д. Е., Финк А. А. Разработка автоматизированных методов порождения служебных документов на естественном языке // Вестн. НГУ. Серия: Информационные технологии. 2017. Т. 15, № 3. С. 79–89. DOI 10.25205/1818-7900-2017-15-3-79-89. ISSN 1818-7900. ru-Ru
dc.identifier.citation Palchunov D. E., Fink A. A. The Development of Automated Methods of Generation of Official Documents in Natural Language. Vestnik NSU. Series: Information Technologies, 2017, vol. 15, no. 3, p. 79–89. DOI 10.25205/1818-7900-2017-15-3-79-89. ISSN 1818-7900. (In Russ.) en
dc.identifier.issn 1818-7900
dc.identifier.other DOI 10.25205/1818-7900-2017-15-3-79-89
dc.identifier.uri https://lib.nsu.ru/xmlui/handle/nsu/13449
dc.description.abstract Статья посвящена проблеме автоматизированного порождения служебных документов на естественном языке с логическим контролем их правильности. Рассмотрены существующие подходы к автоматизации порождения документов. Предложен метод автоматизированного порождения логически правильных документов на естественном языке, основанный на применении параметрических шаблонов. ru_RU
dc.description.abstract The paper is devoted to the problem of automated generation of official documents in natural language with logical control of their correctness. Existing approaches to automation of document generation are considered. We present a method of automated generation of logically correct documents in natural language based on the use of parametric templates. en
dc.language.iso ru ru_RU
dc.publisher Новосибирский государственный университет ru_RU
dc.subject извлечение знаний ru_RU
dc.subject представление знаний ru_RU
dc.subject нормативные документы ru_RU
dc.subject служебные документы ru_RU
dc.subject параметрические шаблоны ru_RU
dc.subject knowledge extraction en
dc.subject knowledge representation en
dc.subject regulatory documents en
dc.subject official documents en
dc.subject parametric templates en
dc.title Разработка автоматизированных методов порождения служебных документов на естественном языке ru_RU
dc.title.alternative The Development of Automated Methods of Generation of Official Documents in Natural Language en
dc.type Article ru_RU
dc.description.reference 1. Деревянко Д. В., Пальчунов Д. Е. Формальные методы разработки вопросно-ответной системы на естественном языке // Вестн. Новосиб. гос. ун-та. Серия: Информационные технологии. 2014. Т. 12, вып. 3. С. 34 –47. 2. Wu F., Weld D. S. Automatically refining the wikipedia infobox ontology // Proc. of the 17th InternationalConference on World Wide Web, WWW. ACM, 2008. Р. 635–644. 3. Lehmann J., Isele R., Jakob M., Jentzsch A., Kontokostas D., Mendes P. N., Hellmann S., Morsey M., Kleef P. van, Auer S., Bizer C. DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia // Semantic Web Journal. 2015. Vol. 6, iss. 2. P. 167–195. 4. Sirin E., Parsia B. SPARQL-DL: SPARQL query for OWL-DL // Third OWL Experiences and Directions Workshop (OWLED). 2007. 5. Palchunov D., Yakhyaeva G., Yasinskaya O. Software system for the diagnosis of thae spine diseases using case-based reasoning // Proc. of the International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON / SibMedInfo – 2015). Novosibirsk, 2015. P. 150–155. 6. Naydanov Ch., Palchunov D., Sazonova P. Development of automated methods for the prevention of risks of critical conditions, based on the analysis of the knowledge extracted from the medical histories // Сиб. науч. мед. журн. 2016. Т. 36, вып. 1. С. 105–113. 7. Palchunov D., Yakhyaeva G., Dolgusheva E. Conceptual Methods for Identifying Needs of Mobile Network Subscribers // Proc. of the 13th International Conference on Concept Lattices and Their Applications. Moscow, 2016. Р. 147–160. 8. Motik B., Shearer R., Horrocks I. Hypertableau reasoning for description logics // Journal of Artificial Intelligence Research. 2009. No. 36. P. 165–228. 9. Сокирко А. В. Семантические словари в автоматической обработке текста (по материалам системы ДИАЛИНГ): Дис. … д-ра техн. наук. М., 2001. 120 с. 10. Леонтьева Н. Н. Русский общесемантический словарь (РОСС): структура, наполнение // НТИ. Сер. 2. 1997. № 12. С. 5–20. ru-Ru
dc.description.reference 1. Derevyanko D. V., Palchunov D. E. Formal methods of development of the questionanswering system on natural language. Vestnik NSU. Series: Information Technologies, 2014, vol. 12, no. 3, p. 34–47. (In Russ.) 2. Wu F., Weld D. S. Automatically refining the wikipedia infobox ontology. Proc. of the 17th InternationalConference on World Wide Web, WWW. ACM, 2008, p. 635–644. 3. Lehmann J., Isele R., Jakob M., Jentzsch A., Kontokostas D., Mendes P. N., Hellmann S. Morsey M., Kleef P. van, Auer S., Bizer C. DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web Journal, 2015, vol. 6, iss. 2, p. 167–195. 4. Sirin E., Parsia B. SPARQL-DL: SPARQL query for OWL-DL. Third OWL Experiences and Directions Workshop (OWLED), 2007. 5. Palchunov D., Yakhyaeva G., Yasinskaya O. Software system for the diagnosis of thae spine diseases using case-based reasoning. Proc. of the International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON / SibMedInfo – 2015). Novosibirsk, 2015, p. 150–155. 6. Naydanov Ch., Palchunov D., Sazonova P. Development of automated methods for the prevention of risks of critical conditions, based on the analysis of the knowledge extracted from the medical histories. Siberian Scientific Medical Journal, 2016, vol. 36, iss. 1, p. 105–113. 7. Palchunov D., Yakhyaeva G., Dolgusheva E. Conceptual Methods for Identifying Needs of Mobile Network Subscribers. Proc. of the 13th International Conference on Concept Lattices and Their Applications. Moscow, 2016, р. 147–160. 8. Motik B., Shearer R., Horrocks I. Hypertableau reasoning for description logics. Journal of Artificial Intelligence Research, 2009, no. 36, p. 165–228. 9. Sokirko A. V. Semantic dictionaries in automatic text parsing (On materials of the system DIALING): Dissertation of Doctor of Technical Sciences. Moscow, 2001, 120 p. (In Russ.) 10. Leonteva N. N. Russian all-semantic dictionary (ROSS): structure, filling. NTI. Series 2, 1997, no. 12, p. 5–20. (In Russ.) en
dc.subject.udc 004.825
dc.relation.ispartofvolume 15
dc.relation.ispartofnumber 3
dc.relation.ispartofpages 79–89


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