Identification and extraction of relationships between entities using dependency trees
Keywords:
Relation extraction, Dependency trees, Political news
Abstract
In 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.
References
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Wu, 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.
Yan, 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.
Bunescu, 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.
Etzioni, 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
Fundel, K., Kuffner, R., & Zimmer, R. (2007). RelEx--Relation extraction using dependency parse trees. Bioinformatics, 23(3). https://doi.org/10.1093/bioinformatics/btl616
Hasegawa, 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.
Li, J., Luong, M.-T., Jurafsky, D., & Hovy, E. (2015). When Are Tree Structures Necessary for Deep Learning of Representations? ArXiv Preprint.
Miwa, M., & Bansal, M. (2016). End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. ArXiv Preprint.
Quan, 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
Vo, 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
Wu, 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.
Yan, 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.
How to Cite
Ramos-Flores, O., & Pinto, D. (2021). Identification and extraction of relationships between entities using dependency trees. Revista Colombiana De Computación, 22(2), 22–36. https://doi.org/10.29375/25392115.4294
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Published
2021-12-01
Section
Article of scientific and technological research
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