How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction

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dc.creatorPeña-Torres, Jefferson A.
dc.creatorGutiérrez, Raúl E.
dc.creatorBucheli, Víctor A.
dc.creatorGonzález, Fabio A.
dc.date2019-12-05
dc.date.accessioned2025-10-01T23:52:17Z
dc.descriptionIn this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.en-US
dc.descriptionEn este trabajo estudiamos el problema de extracción de relaciones del Procesamiento de Lenguaje Natural (PLN). Realizamos una configuración para la adaptación de dominio sin recursos externos. De esta forma, entrenamos un modelo con aprendizaje profundo (DL) para la extracción de relaciones (RE). El modelo permite extraer relaciones semánticas para el dominio biomédico. Sin embargo, ¿El modelo puede ser aplicado a diferentes dominios? El modelo debería adaptarse automáticamente para la extracción de relaciones entre diferentes dominios usando la red de DL. Entrenar completamente modelos DL en una escala de tiempo corta no es práctico, deseamos que los modelos se adapten rápidamente de diferentes conjuntos de datos con varios dominios y sin demora. Así, la adaptación es crucial para los sistemas inteligentes que operan en el mundo real, donde los factores cambiantes y las perturbaciones imprevistas son habituales. En este artículo, presentamos un análisis detallado del problema, una experimentación preliminar, resultados y la discusión acerca de los resultados.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483
dc.identifier10.22430/22565337.1483
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7717
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1472
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1562
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1483/1567
dc.relation/*ref*/D. Zeng, K. Liu, S. Lai, G. Zhou, and J. Zhao, “Relation Classification via Convolutional Deep Neural Network,” in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, 2014, pp. 2335–2344. Avaliable: https://www.aclweb.org/anthology/C14-1220/
dc.relation/*ref*/Y. Lin, S. Shen, Z. Liu, H. Luan, and M. Sun, “Neural Relation Extraction with Selective Attention over Instances,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlín, 2016, vol. 1, pp. 2124–2133. http://dx.doi.org/10.18653/v1/P16-1200
dc.relation/*ref*/X. Ren et al., “Cotype: Joint extraction of typed entities and relations with knowledge bases,” in Proceedings of the 26th International Conference on World Wide Web, Perth, 2017, pp. 1015–1024. http://doi.org/10.1145/3038912.3052708
dc.relation/*ref*/K. Toutanova, D. Chen, P. Pantel, H. Poon, P. Choudhury, and M. Gamon, “Representing Text for Joint Embedding of Text and Knowledge Bases,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015, pp. 1499–1509. http://dx.doi.org/10.18653/v1/D15-1174
dc.relation/*ref*/N. Konstantinova, “Review of relation extraction methods: What is new out there?” in International Conference on Analysis of Images, Social Networks and Texts, Switzerland 2014, pp. 15–28. http://doi.org/10.1007/978-3-319-12580-0_2
dc.relation/*ref*/N. Kambhatla, “Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations,” in Proceedings of the ACL 2004 on Interactive poster and demonstration sessions -, Barcelona, 2004, pp. 1 - 4. https://doi.org/10.3115/1219044.1219066
dc.relation/*ref*/R. C. Bunescu and R. J. Mooney, “A shortest path dependency kernel for relation extraction,” in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT ’05, Vancouver, 2005, pp. 724–731. Avaliable: https://www.aclweb.org/anthology/H05-1091/
dc.relation/*ref*/R. J. Mooney and R. C. Bunescu, “Subsequence kernels for relation extraction,” in Advances in neural information processing systems, 2006, pp. 171–178. Avaliable: http://papers.nips.cc/paper/2787-subsequence-kernels-for-relation-extraction.pdf
dc.relation/*ref*/M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, “Open information extraction from the web.,” in IJCAI, 2007, vol. 7, pp. 2670–2676. Avaliable: https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf
dc.relation/*ref*/R. Socher, B. Huval, C. D. Manning, and A. Y. Ng, “Semantic compositionality through recursive matrix-vector spaces,” Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, 2012, pp. 1201–1211. Avaliable: https://www.aclweb.org/anthology/D12-1110/
dc.relation/*ref*/D. Zhang and D. Wang, “Relation Classification: CNN or RNN?,” in Natural Language Understanding and Intelligent Applications, Springer, Kunming, 2016, pp. 665–675. https://doi.org/10.1007/978-3-319-50496-4_60
dc.relation/*ref*/S. Lim and J. Kang, “Chemical–gene relation extraction using recursive neural network,” Database, vol. 2018, Jun. 2018. https://doi.org/10.1093/database/bay060
dc.relation/*ref*/Y. Xu, L. Mou, G. Li, Y. Chen, H. Peng, and Z. Jin, “Classifying relations via long short term memory networks along shortest dependency paths,” in proceedings of the 2015 conference on empirical methods in natural language processing, Lisboa, 2015, pp. 1785–1794. http://doi.org/10.18653/v1/d15-1206
dc.relation/*ref*/S. Zhang, D. Zheng, X. Hu, and M. Yang, “Bidirectional long short-term memory networks for relation classification,” in Proceedings of the 29th Pacific Asia conference on language, information and computation, Shanghai, 2015, pp. 73–78. Avaliable: https://www.aclweb.org/anthology/Y15-1009.pdf
dc.relation/*ref*/R. Zhang, F. Meng, Y. Zhou, and B. Liu, “Relation classification via recurrent neural network with attention and tensor layers,” Big Data Min. Anal., vol. 1, no. 3, pp. 234–244, Sep. 2018. https://doi.org/10.26599/BDMA.2018.9020022
dc.relation/*ref*/T. H. Nguyen and R. Grishman, “Relation Extraction: Perspective from Convolutional Neural Networks,” in Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, Denver, 2015, pp. 39–48. https://doi.org/10.3115/v1/W15-1506
dc.relation/*ref*/C. dos Santos, B. Xiang, and B. Zhou, “Classifying Relations by Ranking with Convolutional Neural Networks,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, 2015, pp. 626–634. https://doi.org/10.3115/v1/P15-1061
dc.relation/*ref*/K. Xu, Y. Feng, S. Huang, and D. Zhao, “Semantic relation classification via convolutional neural networks with simple negative sampling,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015, pp. 536-540. http://doi.org/10.18653/v1/d15-1062
dc.relation/*ref*/A. Airola, S. Pyysalo, J. Björne, T. Pahikkala, F. Ginter, and T. Salakoski, “All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning,” BMC Bioinformatics, vol. 9, no. S 2, pp. 1-12, Nov. 2008. https://doi.org/10.1186/1471-2105-9-S11-S2
dc.relation/*ref*/S. Kim, J. Yoon, J. Yang, and S. Park, “Walk-weighted subsequence kernels for protein-protein interaction extraction,” BMC Bioinformatics, vol. 11, no. 107, pp. 112–119, Feb. 2010. https://doi.org/10.1186/1471-2105-11-107
dc.relation/*ref*/I. Segura-Bedmar, P. Martinez, and C. de Pablo-Sánchez, “Using a shallow linguistic kernel for drug–drug interaction extraction,” J. Biomed. Inform., vol. 44, no. 5, pp. 789–804, Oct. 2011. https://doi.org/10.1016/j.jbi.2011.04.005
dc.relation/*ref*/Y. Zhang, H. Lin, Z. Yang, J. Wang, and Y. Li, “A single kernel-based approach to extract drug-drug interactions from biomedical literature,” PLoS One, vol. 7, no. 11, pp. e48901, Nov. 2012. https://doi.org/10.1371/journal.pone.0048901
dc.relation/*ref*/K. Hashimoto, M. Miwa, Y. Tsuruoka, and T. Chikayama, “Simple customization of recursive neural networks for semantic relation classification,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, 2013, pp. 1372–1376. Avaliable: https://www.aclweb.org/anthology/D13-1137/
dc.relation/*ref*/Y. Shen and X. Huang, “Attention-based convolutional neural network for semantic relation extraction,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 2016, pp. 2526–2536. Avaliable: https://www.aclweb.org/anthology/C16-1238/
dc.relation/*ref*/L. Wang, Z. Cao, G. de Melo, and Z. Liu, “Relation classification via multi-level attention cnns,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, 2016, vol. 1, pp. 1298–1307. http://doi.org/10.18653/v1/P16-1123
dc.relation/*ref*/J. Lee, S. Seo, and Y. S. Choi, “Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing,” Symmetry, vol. 11, no. 6, Jun. 2019. https://doi.org/10.3390/sym11060785
dc.relation/*ref*/M. Xiao and C. Liu, “Semantic relation classification via hierarchical recurrent neural network with attention,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 2016, pp. 1254–1263. Avaliable: https://www.aclweb.org/anthology/C16-1119/
dc.relation/*ref*/P. Zhou et al., “Attention-based bidirectional long short-term memory networks for relation classification,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, 2016, pp. 207–212. http://doi.org/10.18653/v1/p16-2034
dc.relation/*ref*/R. Cai, X. Zhang, and H. Wang, “Bidirectional recurrent convolutional neural network for relation classification,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, 2016, pp. 756–765. http://doi.org/10.18653/v1/p16-1072
dc.relation/*ref*/Y. Xu et al., “Improved relation classification by deep recurrent neural networks with data augmentation,” ArXiv Prepr., Oct. 2016. Available: https://arxiv.org/abs/1601.03651
dc.relation/*ref*/Y. Liu, F. Wei, S. Li, H. Ji, M. Zhou, and H. Wang, “A dependency-based neural network for relation classification,” ArXiv Prepr., pp.1-10, Jul. 2015. Available: https://arxiv.org/pdf/1507.04646.pdf
dc.relation/*ref*/M. Yu, M. Gormley, and M. Dredze, “Factor-based compositional embedding models.” In NIPS Workshop on Learning Semantics, 2014, pp. 95-101. Available: https://www.cs.cmu.edu/~mgormley/papers/yu+gormley+dredze.nipsw.2014.pdf
dc.relation/*ref*/S. Lai, L. Xu, K. Liu, and J. Zhao, “Recurrent convolutional neural networks for text classification,” in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, 2015, pp. 2267-2273. Available: https://dl.acm.org/citation.cfm?id=2886636
dc.relation/*ref*/D. Zeng, K. Liu, Y. Chen, and J. Zhao, “Distant supervision for relation extraction via piecewise convolutional neural networks,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015, pp. 1753–1762. http://dx.doi.org/10.18653/v1/D15-1203
dc.relation/*ref*/S. Pawar, G. K. Palshikar, and P. Bhattacharyya, “Relation Extraction: A Survey,” ArXiv Prepr. ArXiv171205191, Dec. 2017. Available: https://arxiv.org/pdf/1712.05191.pdf
dc.relation/*ref*/J. Legrand et al., “PGxCorpus: A Manually Annotated Corpus for Pharmacogenomics,” bioRxiv, Jan. 2019. https://doi.org/10.1101/534388
dc.relation/*ref*/Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998. http://doi.org/10.1109/5.726791
dc.relation/*ref*/T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems, Lake Tahoe, Nevada, 2013, pp. 3111–3119. Available: https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
dc.relation/*ref*/J. Pennington, R. Socher, and C. Manning, “Glove: Global Vectors for Word Representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1532–1543. http://dx.doi.org/10.3115/v1/D14-1162
dc.relation/*ref*/J. Turian, L. Ratinov, and Y. Bengio, “Word representations: a simple and general method for semi-supervised learning,” in Proceedings of the 48th annual meeting of the association for computational linguistics, Uppsala, 2010, pp. 384–394. Avaliable: https://www.aclweb.org/anthology/P10-1040/
dc.relation/*ref*/P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Trans. Assoc. Comput. Linguist., vol. 5, pp. 135–146, Jun. 2017. http://dx.doi.org/10.1162/tacl_a_00051
dc.relation/*ref*/S. Pyysalo et al., “BioInfer: a corpus for information extraction in the biomedical domain,” BMC Bioinformatics, vol. 8, no. 50, Feb. 2007. https://doi.org/10.1186/1471-2105-8-50
dc.relation/*ref*/H. Gurulingappa, A. M. Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, and L. Toldo, “Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports,” J. Biomed. Inform., vol. 45, no. 5, pp. 885–892, Oct. 2012. http://dx.doi.org/10.1016/j.jbi.2012.04.008
dc.relation/*ref*/J. Kringelum, S. K. Kjaerulff, S. Brunak, O. Lund, T. I. Oprea, and O. Taboureau, “ChemProt-3.0: a global chemical biology diseases mapping,” Database, Feb. 2016. https://doi.org/10.1093/database/bav123
dc.relation/*ref*/I. Hendrickx et al., “Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals” in Proceedings of the Workshop on Semantic Evaluations, Uppsala, 2010, pp. 33–38. Avaliable: https://www.aclweb.org/anthology/S10-1006/
dc.relation/*ref*/B. Hachey, C. Grover, and R. Tobin, “Datasets for generic relation extraction” Nat. Lang. Eng., vol. 18, no. 1, pp. 21–59, Jan. 2012. https://doi.org/10.1017/s1351324911000106
dc.relation/*ref*/T. Ming Harry Hsu, W. Yu Chen, C.-A. Hou, Y.-H. Hubert Tsai, Y.-R. Yeh, and Y.-C. Frank Wang, “Unsupervised domain adaptation with imbalanced cross-domain data,” in Proceedings of the IEEE International Conference on Computer Vision, Santiago de chile, 2015, pp. 4121–4129. http://doi.org/10.1109/iccv.2015.469
dc.rightsDerechos de autor 2019 TecnoLógicases-ES
dc.sourceTecnoLógicas; Vol. 22 (2019): Special issue-2019; 49-62en-US
dc.sourceTecnoLógicas; Vol. 22 (2019): Edición especial-2019; 49-62es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectSemantic Extractionen-US
dc.subjectDeep Learningen-US
dc.subjectRelation Extractionen-US
dc.subjectNatural Language Processingen-US
dc.subjectExtracción semánticaes-ES
dc.subjectAprendizaje profundoes-ES
dc.subjectExtracción de relacioneses-ES
dc.subjectProcesamiento de lenguaje naturales-ES
dc.titleHow to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extractionen-US
dc.titleCómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicases-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

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