The information system for identification of content set based on analysis of similar texts

dc.citation.epage127
dc.citation.spage122
dc.contributor.affiliationLviv Polytechnic National University, Lviv, Ukraine
dc.contributor.affiliationSilesian University of Technologyty, Gliwice, Poland
dc.contributor.affiliationDrohobych Ivan Franko State Pedagogical University, Drohobych, Ukraine
dc.contributor.authorKovalchuk, Viktoriia
dc.contributor.authorLytvyn, Vasyl
dc.contributor.authorVysotska, Victoria
dc.contributor.authorHrendus, Mariya
dc.contributor.authorNaum, Oleh
dc.coverage.placenameLviv
dc.coverage.temporal25-27 June 2018
dc.date.accessioned2018-09-03T11:41:09Z
dc.date.available2018-09-03T11:41:09Z
dc.date.created2018-06-25
dc.date.issued2018-06-25
dc.description.abstractInformation search is one of the most dynamic areas in terms of development and search for new algorithms for solving this problem. Nowadays a large number of search engines, which work with artistic composition, have been developed, but most of them are oriented only on conducting the work with metadata. In addition, effective methods of analysis of the subject of the text have been used, as well as identification of works after it is considered. Different ways of searching and problems of implementation of an ontologically flexible semantic search are considered. An information system for the identification of a plurality of content has been developed based on a thematic analysis of similar texts.
dc.format.extent122-127
dc.format.pages6
dc.identifier.citationThe information system for identification of content set based on analysis of similar texts / Viktoriia Kovalchuk, Vasyl Lytvyn, Victoria Vysotska, Mariya Hrendus, Oleh Naum // Computational linguistics and intelligent systems, 25-27 June 2018. — Lviv : Lviv Polytechnic National University, 2018. — Vol 2 : Workshop. — P. 122–127. — (Part 2. Workshop conference tracks. Section I. Computational Linguistics).
dc.identifier.citationenThe information system for identification of content set based on analysis of similar texts / Viktoriia Kovalchuk, Vasyl Lytvyn, Victoria Vysotska, Mariya Hrendus, Oleh Naum // Computational linguistics and intelligent systems, 25-27 June 2018. — Lviv : Lviv Polytechnic National University, 2018. — Vol 2 : Workshop. — P. 122–127. — (Part 2. Workshop conference tracks. Section I. Computational Linguistics).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/42558
dc.language.isoen
dc.publisherLviv Polytechnic National University
dc.relation.ispartofComputational linguistics and intelligent systems (2), 2018
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dc.relation.referencesen2. Bontcheva, K., Tablan, V., Cunningham, H., Semantic search over documents and ontologies. Bridging Between Information Retrieval and Databases, Springer (2014)
dc.relation.referencesen3. Taylor, William P., A comparative study on ontology generation and text clustering using VSM, LSI, and document ontology models. Clemson University (2007)
dc.relation.referencesen4. Giannopoulos, G., A tool for semantic annotation and search. In: The Semantic Web: Research and Applications, Springer Berlin Heidelberg, 376-380 (2010)
dc.relation.referencesen5. Berlanga, R., Nebot ,V., Pérez, M., Tailored semantic annotation for semantic search .Web Semantics: Science, Services and Agents on the World Wide Web, 69-81 (2015)
dc.relation.referencesen6. Saša, Neši: Semantic Document Architecture for Desktop Data Integration and Management: Doctoral Dissertation (2010)
dc.relation.referencesen7. Hotho, A., Staab, S., Stumme, G., Wordnet improves Text Document Clustering. In: Proc. of the SIGIR 2003 Semantic Web Workshop, 541–544 (2003)
dc.relation.referencesen8. Vysotska, V., Chyrun, L., Lytvyn, V., Methods based on ontologies for information resources processing. Germany: LAP LAMBERT Academic Publishing (2016).
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dc.relation.referencesen10. Vysotska, V., Lytvyn, V., Web resources processing based on ontologies. Saarbrücken, Germany: LAP LAMBERT Academic Publishing (2018)
dc.relation.referencesen11. Vysotska, V., Shakhovska, N., Information technologies of gamification for training and recruitment. Saarbrücken, Germany: LAP LAMBERT Academic Publishing (2018)
dc.relation.referencesen12. Vysotska, V., Internet systems design and development based on Web Mining and NLP. Saarbrücken, Germany: LAP LAMBERT Academic Publishing (2018)
dc.relation.referencesen13. Vysotska, V., Computer linguistics for online marketing in information technology : Monograph. Saarbrücken, Germany: LAP LAMBERT Academic Publishing (2018)
dc.relation.referencesen14. Lytvyn, V., Vysotska, V., Chyrun, L., Smolarz, A., Naum O., Intelligent System Structure for Web Resources Processing and Analysis. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 56-74 (2017)
dc.relation.referencesen15. Lytvyn, V., Vysotska, V., Wojcik, W., Dosyn, D., A Method of Construction of Automated Basic Ontology. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 75-83 (2017)
dc.relation.referencesen16. Lytvynenko, V., Lurie, I., Radetska, S., Voronenko, M., Kornilovska, N., Partenjucha, D., Content analysis of some social media of the occupied territories of Ukraine. In: 1st Inter.l Conference Computational Linguistics and Intelligent Systems, COLINS, 84–94 (2017)
dc.relation.referencesen17. Shepelev, G., Khairova, N., Methods of comparing interval objects in intelligent computer systems. . In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 100–109 (2017)
dc.relation.referencesen18. Orobinska, O., Chauchat, J.-H., Sharonova, N., Methods and models of automatic ontology construction for specialized domains (case of the Radiation Security). In: 1st Inter. Conf. Computational Linguistics and Intelligent Systems, COLINS, 95–99 (2017)
dc.relation.referencesen19. Hamon, T., Grabar, N., Unsupervised acquisition of morphological resources for Ukrainian. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 20–30 (2017)
dc.relation.referencesen20. Grabar, N., Hamon, T., Creation of a multilingual aligned corpus with Ukrainian as the target language and its exploitation. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 10–19 (2017)
dc.relation.referencesen21. Hamon, T., Biomedical text mining. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wpcontent/ uploads/2017/04/2017COLINS-THAMON-keynote.pdf (2017)
dc.relation.referencesen22. Lande, D., Andrushchenko, V., Balagura, I., An index of authors’ popularity for Internet encyclopedia. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 47–55 (2017)
dc.relation.referencesen23. Lande, D., Creation of subject domain models on the basis of monitoring of network information resources. In: 1st Inter. Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wp-content/uploads/2017/04/Lande.pdf (2017)
dc.relation.referencesen24. Protsenko, Y., Intuition on modern deep learning approaches in computer vision. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wp-content/uploads/2017/04/protsenko.pdf (2017)
dc.relation.referencesen25. Kolbasin, V., AI trends, or brief highlights of NIPS 2016. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wpcontent/ uploads/2017/04/CoLlnS_TuS.pdf (2017)
dc.relation.referencesen26. Kersten, W., The Digital Transformation of the Industry – the Logistics Example. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wp-content/uploads/2017/04/CoLlnS_TuS.pdf (2017)
dc.relation.referencesen27. Shalimov, V., Big Data – Revolution in Data Storage and Processing. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wp-content/uploads/2017/04/BigData_eng.pdf (2017)
dc.relation.referencesen28. Hnot, T., Qualitative content analysis: expertise and case study. In: 1st Inter. Conference Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wpcontent/ uploads/2017/04/Qualitative-content-analysis_expertise-and-case-study.pdf (2017)
dc.relation.referencesen29. Romanyshyn, M., Grammatical Error Correction: why commas matter. In: 1st Inter. Conf. Computational Linguistics and Intelligent Systems, COLINS, http://colins.in.ua/wpcontent/ uploads/2017/04/Grammatical-Error-Correction-why-commas-matter.pdf. (2017)
dc.relation.referencesen30. Yukhno, K., Chubar, E., Gamification: today and tomorrow. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 139–140 (2017)
dc.relation.referencesen31. Pidpruzhnikov, V., Ilchenko, M., Search optimization and localization of the website of Department of Applied Linguistics. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 137–138 (2017)
dc.relation.referencesen32. Olifenko, I., Borysova, N., Analysis of existing German Corpora. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 135–136 (2017)
dc.relation.referencesen33. Kolesnik, A., Khairova, N., Use of linguistic criteria for estimating of wikipedia articles quality. In: 1st Inter. Conf. Computational Linguistics and Intelligent Systems, 133–134
dc.relation.referencesen34. Kirkin, S., Melnyk, K., Intelligent data processing in creating targeted advertising. In: 1st Inter. Conf. Computational Linguistics and Intelligent Systems, COLINS, 131–132 (2017)
dc.relation.referencesen35. Hordienko, H., Ilchenko, M., Development and computerization of an English term system in the fields of drilling and drilling rigs. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 129–130 (2017)
dc.relation.referencesen36. Gorbachov, V., Cherednichenko, O., Improving communication in enterprise solutions: challenges and opportunities. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 127–128 (2017)
dc.relation.referencesen37. Didusov, V., Kochueva, Z., Statistical methods usage of descriptive statistics in corpus linguistic. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 125–126 (2017)
dc.relation.referencesen38. Verbinenko, Yu., Discursive units in scientific texts. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 120–123 (2017)
dc.relation.referencesen39. Titova, V., Gnatchuk, I., Evaluation ofa formalized model for classification of emergency situations. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 110–119 (2017)
dc.relation.referencesen40. Kuprianov, Ye., Semantic state superpositions and their treatment in virtual lexicographic laboratory for spanish language dictionary. In: 1st International Conference Computational Linguistics and Intelligent Systems, COLINS, 37–46 (2017)
dc.relation.referencesen41. Kotov, M., NLP resources for a rare language morphological analyzer: danish case. In: 1st Inter, Conf, Computational Linguistics and Intelligent Systems, COLINS, 31–36 (2017)
dc.relation.urihttp://colins.in.ua/wpcontent/
dc.relation.urihttp://colins.in.ua/wp-content/uploads/2017/04/Lande.pdf
dc.relation.urihttp://colins.in.ua/wp-content/uploads/2017/04/protsenko.pdf
dc.relation.urihttp://colins.in.ua/wp-content/uploads/2017/04/CoLlnS_TuS.pdf
dc.relation.urihttp://colins.in.ua/wp-content/uploads/2017/04/BigData_eng.pdf
dc.rights.holder© 2018 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectsemantic
dc.subjectsemantic search
dc.subjecttext search
dc.subjectinformation search
dc.subjectontology
dc.subjectinformation
dc.subjecte-library
dc.subjectelectronic library
dc.subjectsemantic electronic library
dc.subjecttext analysis
dc.subjectsearch
dc.titleThe information system for identification of content set based on analysis of similar texts
dc.typeConference Abstract

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