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Kumar, \u201cA survey of deep learning methods for relation extraction,\u201d arXiv preprint arXiv:1705.03645, 2017."},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] D. Zhou, D. Zhong, and Y. He, \u201cBiomedical relation extraction: from binary to complex,\u201d Computational and mathematical methods in medicine, vol.2014, pp.1-18, 2014. 10.1155\/2014\/298473","DOI":"10.1155\/2014\/298473"},{"key":"7","unstructured":"[7] E. Boschee, R. Weischedel, and A. Zamanian, \u201cAutomatic information extraction,\u201d Proceedings of the International Conference on Intelligence Analysis, Citeseer, 2005."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] F.M. Suchanek, G. Ifrim, and G. Weikum, \u201cCombining linguistic and statistical analysis to extract relations from web documents,\u201d Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.712-717, ACM, 2006. 10.1145\/1150402.1150492","DOI":"10.1145\/1150402.1150492"},{"key":"9","unstructured":"[9] Y.S. Chan and D. Roth, \u201cExploiting background knowledge for relation extraction,\u201d Proceedings of the 23rd International Conference on Computational Linguistics, pp.152-160, Association for Computational Linguistics, 2010."},{"key":"10","unstructured":"[10] D. Zeng, K. Liu, S. Lai, G. Zhou, J. Zhao, \u201cRelation classification via convolutional deep neural network,\u201d COLING, pp.2335-2344, 2014."},{"key":"11","unstructured":"[11] D. Zhang and D. Wang, \u201cRelation classification via recurrent neural network,\u201d arXiv preprint arXiv:1508.01006, 2015."},{"key":"12","unstructured":"[12] C.N.d. Santos, B. Xiang, and B. 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Rosario and M.A. Hearst, \u201cMulti-way relation classification: application to protein-protein interactions,\u201d Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp.732-739, 2005. 10.3115\/1220575.1220667","DOI":"10.3115\/1220575.1220667"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] J.-D. Kim, T. Ohta, and J. Tsujii, \u201cCorpus annotation for mining biomedical events from literature,\u201d BMC bioinformatics, vol.9, no.1, p.10, 2008. 10.1186\/1471-2105-9-10","DOI":"10.1186\/1471-2105-9-10"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] G. Angeli, J. Tibshirani, J. Wu, and C.D. Manning, \u201cCombining distant and partial supervision for relation extraction,\u201d EMNLP, pp.1556-1567, 2014. 10.3115\/v1\/d14-1164","DOI":"10.3115\/v1\/D14-1164"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Z. GuoDong, S. Jian, Z. Jie, and Z. 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Takeo, \u201cWord sense disambiguation and text segmentation based on lexical cohesion,\u201d Proceedings of the 15th conference on Computational linguistics-Volume 2, Association for Computational Linguistics, pp.755-761, 1994. 10.3115\/991250.991268","DOI":"10.3115\/991250.991268"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] T. Wang, Y. Li, K. Bontcheva, H. Cunningham, and J. Wang, \u201cAutomatic extraction of hierarchical relations from text,\u201d European Semantic Web Conference, Springer, vol.4011, pp.215-229, 2006. 10.1007\/11762256_18","DOI":"10.1007\/11762256_18"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. 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