https://oldena.lpnu.ua/handle/ntb/56806| Title: | Unsupervised Open Relation Extraction |
| Authors: | Tarasenko, Yaroslav Petrasova, Svitlana |
| Affiliation: | National Technical University “Kharkiv Polytechnic Institute” |
| Bibliographic description (Ukraine): | Tarasenko 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. |
| Bibliographic description (International): | Tarasenko 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. |
| Is part of: | Computational linguistics and intelligent systems, 2021 |
| Issue Date: | 4-May-2021 |
| Place of the edition/event: | Львів ; Харків Lviv ; Kharkiv |
| Temporal Coverage: | 22-23 April 2021, Kharkiv |
| Keywords: | Information Extraction Open Relation Extraction semantic relation TF-IDF parsing cluster analysis |
| Number of pages: | 2 |
| Page range: | 93-94 |
| Start page: | 93 |
| End page: | 94 |
| Abstract: | The 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/56806 |
| ISSN: | 2523-4013 |
| Copyright owner: | copyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). © 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and |
| URL for reference material: | https://doi.org/10.1155/2018/4929674 http://www.isa.ru/aidt/images/documents/2018-02/47-61.pdf |
| References (Ukraine): | [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. [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. [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 [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 [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. |
| References (International): | [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. [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. [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 [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 [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. |
| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2021 р. |
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