Unsupervised Open Relation Extraction

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dc.citation.epage94
dc.citation.spage93
dc.contributor.affiliationNational Technical University “Kharkiv Polytechnic Institute”
dc.contributor.authorTarasenko, Yaroslav
dc.contributor.authorPetrasova, Svitlana
dc.coverage.placenameЛьвів ; Харків
dc.coverage.placenameLviv ; Kharkiv
dc.coverage.temporal22-23 April 2021, Kharkiv
dc.date.accessioned2022-05-23T10:50:12Z
dc.date.available2022-05-23T10:50:12Z
dc.date.created2021-05-04
dc.date.issued2021-05-04
dc.description.abstractThe paper describes an approach to open relation extraction based on unsupervised machine learning. The state-of-the-art methods for extracting semantic relations are analyzed. The algorithm of automatic open relation extraction using statistical, syntactic and contextual information is proposed. The results of the study can be used in information retrieval, summarization, machine translation, question-answering systems, etc.
dc.format.extent93-94
dc.format.pages2
dc.identifier.citationTarasenko Y. Unsupervised Open Relation Extraction / Yaroslav Tarasenko, Svitlana Petrasova // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 93–94.
dc.identifier.citationenTarasenko Y. Unsupervised Open Relation Extraction / Yaroslav Tarasenko, Svitlana Petrasova // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 93–94.
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56806
dc.language.isoen
dc.relation.ispartofComputational linguistics and intelligent systems, 2021
dc.relation.references[1] O. Shanidze, S. Petrasova, Extraction of Semantic Relations from Wikipedia Text Corpus, in: Proceedings of 3rd International Conference: Computational Linguistics and Intelligent Systems (CoLInS 2019), Kharkiv, Ukraine, 2019, pp. P. 74–75.
dc.relation.references[2] Peiqian Liu, Xiaojie Wang, A Semieager Classifier for Open Relation Extraction, in: Mathematical Problems in Engineering, 2018. doi: https://doi.org/10.1155/2018/4929674.
dc.relation.references[3] F. Petroni, L.D. Corro, R. Gemulla, CORE: Context-Aware Open Relation Extraction with Factorization Machines, in: Association for Computational Linguistics, 2015. doi: 10.18653/v1/d15-1204
dc.relation.references[4] A.O. Shelmanov, V.A. Isakov, M.A. Stankevich, I.V. Smirnov, Open information extraction from texts. Part I. Statement of the problem and overview of methods, in: Artificial Intelligence And Decision Making, 2018, pp. 47-61. URL: http://www.isa.ru/aidt/images/documents/2018-02/47-61.pdf
dc.relation.references[5] D.S. Batista, B. Martins, M. J. Silva, Semi-supervised bootstrapping of relationship extractors with distributional semantics, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 499–504.
dc.relation.referencesen[1] O. Shanidze, S. Petrasova, Extraction of Semantic Relations from Wikipedia Text Corpus, in: Proceedings of 3rd International Conference: Computational Linguistics and Intelligent Systems (CoLInS 2019), Kharkiv, Ukraine, 2019, pp. P. 74–75.
dc.relation.referencesen[2] Peiqian Liu, Xiaojie Wang, A Semieager Classifier for Open Relation Extraction, in: Mathematical Problems in Engineering, 2018. doi: https://doi.org/10.1155/2018/4929674.
dc.relation.referencesen[3] F. Petroni, L.D. Corro, R. Gemulla, CORE: Context-Aware Open Relation Extraction with Factorization Machines, in: Association for Computational Linguistics, 2015. doi: 10.18653/v1/d15-1204
dc.relation.referencesen[4] A.O. Shelmanov, V.A. Isakov, M.A. Stankevich, I.V. Smirnov, Open information extraction from texts. Part I. Statement of the problem and overview of methods, in: Artificial Intelligence And Decision Making, 2018, pp. 47-61. URL: http://www.isa.ru/aidt/images/documents/2018-02/47-61.pdf
dc.relation.referencesen[5] D.S. Batista, B. Martins, M. J. Silva, Semi-supervised bootstrapping of relationship extractors with distributional semantics, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 499–504.
dc.relation.urihttps://doi.org/10.1155/2018/4929674
dc.relation.urihttp://www.isa.ru/aidt/images/documents/2018-02/47-61.pdf
dc.rights.holdercopyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
dc.rights.holder© 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and
dc.subjectInformation Extraction
dc.subjectOpen Relation Extraction
dc.subjectsemantic relation
dc.subjectTF-IDF
dc.subjectparsing
dc.subjectcluster analysis
dc.titleUnsupervised Open Relation Extraction
dc.typeArticle

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