Identificación y extracción de relaciones entre entidades empleando árboles de depende

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dc.contributor.authorRamos Flores, Orlando
dc.contributor.authorPinto, David
dc.contributor.orcidRamos Flores, Orlando [0000-0002-8579-4123]spa
dc.contributor.orcidPinto, David [0000-0002-8516-5925]spa
dc.date.accessioned2024-09-11T22:34:33Z
dc.date.available2024-09-11T22:34:33Z
dc.date.issued2021-09-14
dc.description.abstractEn este trabajo se presenta un enfoque no supervisado para identificar y extraer relaciones entre dos entidades nombradas. El enfoque se conforma por casos, estableciendo un conjunto de patrones para identificar relaciones previamente establecidas. Además, se estudia un conjunto de casos para identificar y extraer relaciones de forma automática. Se emplean las dependencias universales appos y amod, así como los elementos clave de la oración: el verbo entre dos entidades nombradas, y el sujeto y objeto. Este proceso se realiza de forma automática sobre documentos no estructurados en el dominio de noticias políticas en idioma español. Para verificar las relaciones se realizó una evaluación manual sobre un conjunto seleccionado.spa
dc.description.abstractenglishIn this paper, we present an unsupervised approach to identify and extract relationships between two named entities. The approach is made up of cases, establishing a set of patterns to identify previously established relationships. In addition, a set of cases is studied to identify and extract relationships automatically. The universal dependencies appos and amod were used, as well as the sentence’s key elements, such as the verb between two named entities, and the subject and object. This process is carried out automatically on unstructured documents in the domain of political news in Spanish. We made a manual evaluation on a selected set to verify the relationships extracted.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/25392115.4294
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26467
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4294/3505spa
dc.relation.referencesAfzal, N., Mitkov, R., & Farzindar, A. (2011). Unsupervised Relation Extraction Using Dependency Trees for Automatic Generation of Multiple-Choice Questions. In C. Butz & P. Lingras (Eds.), Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science (Vol. 6657). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_4
dc.relation.referencesBunescu, R., & Mooney, R. (2005). Bunescu, Razvan, and Raymond Mooney. "A shortest path dependency kernel for relation extraction. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 724–731.
dc.relation.referencesEtzioni, O., Banko, M., Soderland, S., & Weld, D. S. (2008). Open information extraction from the web. Communications of the ACM, 51(12). https://doi.org/10.1145/1409360.1409378
dc.relation.referencesFundel, K., Kuffner, R., & Zimmer, R. (2007). RelEx--Relation extraction using dependency parse trees. Bioinformatics, 23(3). https://doi.org/10.1093/bioinformatics/btl616
dc.relation.referencesHasegawa, T., Sekine, S., & Grishman, R. (2004). Discovering relations among named entities from large corpora. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), 415–422.
dc.relation.referencesLi, J., Luong, M.-T., Jurafsky, D., & Hovy, E. (2015). When Are Tree Structures Necessary for Deep Learning of Representations? ArXiv Preprint.
dc.relation.referencesMiwa, M., & Bansal, M. (2016). End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. ArXiv Preprint.
dc.relation.referencesQuan, C., Wang, M., & Ren, F. (2014). An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature. PLoS ONE, 9(7). https://doi.org/10.1371/journal.pone.0102039
dc.relation.referencesVo, D.-T., & Bagheri, E. (2017). Open information extraction. In World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence | Semantic Computing. World Scientific Publishing Co Pte Ltd. https://doi.org/10.1142/9789813227927_0001
dc.relation.referencesWu, Y., Zhang, Q., Huang, X., & Wu, L. (2009). Phrase dependency parsing for opinion mining. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 1533–1541.
dc.relation.referencesYan, Y., Okazaki, N., Matsuo, Y., Yang, Z., & Ishizuka, M. (2004). Unsupervised relation extraction by mining wikipedia texts using information from the web. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 1021– 1029.
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/276spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourceVol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 22-36spa
dc.subjectExtracción de relacionesspa
dc.subjectÁrboles de dependenciaspa
dc.subjectNoticias políticasspa
dc.subject.keywordsRelation extractioneng
dc.subject.keywordsDependency treeseng
dc.subject.keywordsPolitical newseng
dc.titleIdentificación y extracción de relaciones entre entidades empleando árboles de dependespa
dc.title.translatedIdentification and extraction of relationships between entities using dependency treeseng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

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