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Weikum, \u201cHYENA: Hierarchical type classification for entity names,\u201d Proc. 24th International Conference on Computational Linguistics, pp.1361-1370, 2012."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] L.D. Corro, A. Abujabal, R. Gemulla, and G. Weikum, \u201cFINET: Context-aware fine-grained named entity typing,\u201d Proc. 2015 Conference on Empirical Methods in Natural Language Processing, pp.868-878, 2015. 10.18653\/v1\/d15-1103","DOI":"10.18653\/v1\/D15-1103"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] X. Ling, S. Singh, and D.S. Weld, \u201cDesign challenges for entity linking,\u201d Transactions of the Association of Computational Linguistics, pp.315-328, 2015.","DOI":"10.1162\/tacl_a_00141"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] G.S. Mann, \u201cFine-grained proper noun ontologies for question answering,\u201d Proc. SemaNet&apos;02: Building and Using Semantic Networks, pp.1-7, 2002. 10.3115\/1118735.1118746","DOI":"10.3115\/1118735.1118746"},{"key":"8","unstructured":"[8] J. Kazama and K. Torisawa, \u201cInducing gazetteers for named entity recognition by large-scale clustering of dependency relations,\u201d Proc. 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp.407-415, 2008."},{"key":"9","unstructured":"[9] A. Carlson, S. Gaffney, and F. Vasile, \u201cLearning a named entity tagger from gazetteers with the partial perceptron,\u201d Proc. 2009 AAAI Spring Symposium on Learning by Reading and Learning to Read, 7 pages, 2009."},{"key":"10","unstructured":"[10] F. Alotaibi and M. Lee, \u201cAutomatically developing a fine-grained arabic named entity corpus and gazetteer by utilizing wikipedia,\u201d Proc. 6th International Joint Conference on Natural Language Processing, pp.392-400, 2013."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A.P. Aprosio, C. Giuliano, and A. Lavelli, \u201cExtending the coverage of DBpedia properties using distant supervision over Wikipedia,\u201d Proc. 1st International Workshop on NLP and DBpedia, pp.20-31, 2013.","DOI":"10.1145\/2494188.2494196"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M. F\u00e4rber, F. Bartscherer, C. Menne, and A. Rettinger, \u201cLinked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO,\u201d Semantic Web, vol.1, pp.1-5, 2016.","DOI":"10.3233\/SW-170275"},{"key":"13","unstructured":"[13] R. Higashinaka, K. Sadamitsu, K. Saito, T. Makino, and Y. Matsuo, \u201cCreating an extended named entity dictionary from Wikipedia,\u201d Proc. 24th International Conference on Computational Linguistics, pp.1163-1178, 2012."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] D. Jargalsaikhan, N. Okazaki, K. Matsuda, and K. Inui, \u201cBuilding a corpus for japanese Wikification with fine-grained entity classes,\u201d Proc. ACL 2016 Student Research Workshop, pp.138-144, 2016. 10.18653\/v1\/p16-3021","DOI":"10.18653\/v1\/P16-3021"},{"key":"15","unstructured":"[15] J. Chang, R. Tzong-Han Tsai, and J.S. Chang, \u201cWikiSense: Supersense tagging of Wikipedia named entities based Wordnet,\u201d Proc. 23rd Pacific Asia Conference on Language, Information, and Computation, pp.72-81, 2009."},{"key":"16","unstructured":"[16] W. Dakka and S. Cucerzan, \u201cAugmenting Wikipedia with named entity tags,\u201d Proc. 3rd International Joint Conference on Natural Language Processing, pp.545-552, 2008."},{"key":"17","unstructured":"[17] S. Tardif, R.J. Curran, and T. Murphy, \u201cImproved text categorisation for Wikipedia named entities,\u201d Proc. Australasian Language Technology Association Workshop 2009, pp.104-108, 2009."},{"key":"18","unstructured":"[18] A. Toral and R. Mu\u00f1oz, \u201cA proposal to automatically build and maintain gazetteers for named entity recognition by using Wikipedia,\u201d Proc. the Workshop on NEW TEXT Wikis and blogs and other dynamic text sources, pp.56-61, 2006."},{"key":"19","unstructured":"[19] Y. Watanabe, M. Asahara, and Y. Matsumoto, \u201cA graph-based approach to named entity categorization in Wikipedia using conditional random fields,\u201d Proc. 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.649-657, 2007."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] R. Caruana, \u201cMultitask learning,\u201d Mach. Learn., vol.28, no.1, pp.41-75, 1997.","DOI":"10.1023\/A:1007379606734"},{"key":"21","unstructured":"[21] T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean, \u201cDistributed representations of words and phrases and their compositionality,\u201d Proc. Advances in Neural Information Processing Systems 26, pp.3111-3119, 2013."},{"key":"22","unstructured":"[22] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel, \u201cThe automatic content extraction (ACE) program tasks, data, and evaluation,\u201d Proc. 4th International Conference on Language Resources and Evaluation, pp.837-840, 2004."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F.M. Suchanek, G. Kasneci, and G. Weikum, \u201cYago: A core of semantic knowledge,\u201d Proc. 16th International World Wide Web Conference, pp.697-706, 2007. 10.1145\/1242572.1242667","DOI":"10.1145\/1242572.1242667"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, \u201cDBpedia: A nucleus for a web of open data,\u201d Proc. 6th International The Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference, pp.722-735, 2007.","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] D. Vrande\u010di\u0107 and M. Kr\u00f6tzsch, \u201cWikidata: A free collaborative knowledge base,\u201d Commun. ACM, vol.57, no.10, pp.78-85, 2014. 10.1145\/2629489","DOI":"10.1145\/2629489"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] X. Ling and D.S. Weld, \u201cFine-grained entity recognition,\u201d Proc. 26th AAAI Conference on Artificial Intelligence, pp.94-100, 2012.","DOI":"10.1609\/aaai.v26i1.8122"},{"key":"27","unstructured":"[27] N. Nakashole, T. Tylenda, and G. Weikum, \u201cFine-grained semantic typing of emerging entities,\u201d Proc. 51st Annual Meeting of the Association for Computational Linguistics, pp.1488-1497, 2013."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] S. Shimaoka, P. Stenetorp, K. Inui, and S. Riedel, \u201cAn attentive neural architecture for fine-grained entity type classification,\u201d Proc. 5th Workshop on Automated Knowledge Base Construction, pp.69-74, 2016. 10.18653\/v1\/w16-1313","DOI":"10.18653\/v1\/W16-1313"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] T. Iwakura, R. Tachibana, and K. Komiya, \u201cConstructing a Japanese basic named entity corpus of various genres,\u201d Proc. the 6th Named Entity Workshop, joint with 54th ACL, pp.41-46, 2016. 10.18653\/v1\/w16-2706","DOI":"10.18653\/v1\/W16-2706"},{"key":"30","unstructured":"[30] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d Proc. 3rd International Conference on Learning Representations, 15 pages, 2015."},{"key":"31","unstructured":"[31] T. Kudo, K. Yamamoto, and Y. Matsumoto, \u201cApplying conditional random fields to japanese morphological analysis,\u201d Proc. 2004 Conference on Empirical Methods in Natural Language Processing, pp.230-237, 2004."},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] T. Fuchi and S. Takagi, \u201cJapanese morphological analyzer using word co-occurrence-JTAG,\u201d Proc. 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, p.409, 1998. 10.3115\/980845.980915","DOI":"10.3115\/980845.980915"},{"key":"33","unstructured":"[33] T. Mikolov and Q. Le, \u201cDistributed representations of sentences and documents,\u201d Proc. 31st International Conference on Machine Learning, pp.1188-1196, 2014."},{"key":"34","unstructured":"[34] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d Proc. Workshop at International Conference on Learning Representations 2013, 9 pages, 2013."},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] S. Godbole and S. Sarawagi, Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004. Proceedings, ch. Discriminative Methods for Multi-labeled Classification, pp.22-30, Springer Berlin Heidelberg, 2004.","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] G. Tsoumakas, I. Katakis, and I. Vlahavas, \u201cMining multi-label data,\u201d in Data mining and knowledge discovery handbook, pp.667-685, Springer, 2009.","DOI":"10.1007\/978-0-387-09823-4_34"},{"key":"37","unstructured":"[37] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, \u201cScikit-learn: Machine learning in Python,\u201d Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011."},{"key":"38","unstructured":"[38] S. Tokui, K. Oono, S. Hido, and J. Clayton, \u201cChainer: A next-generation open source framework for deep learning,\u201d Proc. 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