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For example, given the narrative report \u201cAnna stole Ada\u2019s car\u201d, imagine that we intend to identify the VICTIM and the ROBBER, two sub-labels of PERSON. Traditional NER systems have limited performance in categorizing entity labels arranged in a hierarchical structure. Furthermore, it is unfeasible to obtain information from knowledge bases to give a disambiguated meaning between the entity mentions and the actual labels. This information must be extracted directly from the context dependencies. In this paper, we deal with the Hierarchical Entity-Label Disambiguation problem in Police reports without the use of knowledge bases. To tackle such a problem, we present HELD, an ensemble model that combines two components for NER: a BLSTM-CRF architecture and a NER tool. Experiments conducted on a real Police reports dataset show that HELD significantly outperforms baseline approaches.<\/jats:p>","DOI":"10.3233\/ida-205720","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T17:25:03Z","timestamp":1650993903000},"page":"637-657","source":"Crossref","is-referenced-by-count":5,"title":["HELD: Hierarchical entity-label disambiguation in named entity recognition task using deep learning"],"prefix":"10.1177","volume":"26","author":[{"given":"B\u00e1rbara St\u00e9phanie","family":"Neves Oliveira","sequence":"first","affiliation":[{"name":"Insight Data Science Lab, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Andreza","family":"Fernandes de Oliveira","sequence":"additional","affiliation":[{"name":"Insight Data Science Lab, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Vinicius","family":"Monteiro de Lira","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Technologies, National Research Council, Pisa, Italy"}]},{"given":"Ticiana","family":"Linhares Coelho da Silva","sequence":"additional","affiliation":[{"name":"Insight Data Science Lab, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Jos\u00e9 Ant\u00f4nio","family":"Fernandes de Mac\u00eado","sequence":"additional","affiliation":[{"name":"Insight Data Science Lab, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205720_ref1","doi-asserted-by":"crossref","unstructured":"A. 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Belongie, Class-Balanced Loss Based on Effective Number of Samples, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 9268\u20139277.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"10.3233\/IDA-205720_ref13","unstructured":"T.L.C. da Silva, N. da Silva Arauj\u00fa, J.A.F. de Mac\u00eado, D. Ara\u00fajo, F.M. Soares, P.A. Rego and A.V.L. Neto, Novel approach for label disambiguation via deep learning, in: Machine Learning and Data Mining (MLDM) (2), 2019, pp. 431\u2013442."},{"key":"10.3233\/IDA-205720_ref14","unstructured":"T.L.C. da Silva, N. da Silva Ara\u00fajo, J.A.F. de Mac\u00eado, D. Ara\u00fajo, F.M. Soares, P.A.L. Rego and A.V.L. Neto, Novel approach for label disambiguation via deep learning, in: P. Perner, ed, Machine Learning and Data Mining in Pattern Recognition, 15th International Conference on Machine Learning and Data Mining, MLDM 2019, New York, NY, USA, July 20\u201325, 2019, Proceedings, Volume II, ibai publishing, 2019, pp. 431\u2013442."},{"key":"10.3233\/IDA-205720_ref15","unstructured":"T.L.C. da Silva, R.P. Magalh\u00e3es, J.A. de Mac\u00eado, D. Ara\u00fajo, N. Ara\u00fajo, V. de Melo, P. Ol\u00edmpio, P.A. Rego and A.V.L. Neto, Improving named entity recognition using deep learning with human in the loop, in: Extending Database Technology (EDBT), 2019, pp. 594\u2013597."},{"key":"10.3233\/IDA-205720_ref16","doi-asserted-by":"crossref","unstructured":"L. Del Corro, A. Abujabal, R. Gemulla and G. Weikum, Finet: Context-aware fine-grained named entity typing, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 868\u2013878.","DOI":"10.18653\/v1\/D15-1103"},{"key":"10.3233\/IDA-205720_ref17","unstructured":"J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in: North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (1), 2019."},{"key":"10.3233\/IDA-205720_ref19","doi-asserted-by":"crossref","unstructured":"C. dos Santos, V. Guimaraes, R. Niter\u00f3i and R. de Janeiro, Boosting named entity recognition with neural character embeddings, in: Proceedings of NEWS 2015 The Fifth Named Entities Workshop, 2015, p. 25.","DOI":"10.18653\/v1\/W15-3904"},{"key":"10.3233\/IDA-205720_ref20","doi-asserted-by":"crossref","unstructured":"S. Hakimov, S.A. Oto and E. 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Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, In: ICML \u201901: Proceedings of the Eighteenth International Conference on Machine Learning, 2001."},{"key":"10.3233\/IDA-205720_ref27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.engappai.2019.05.007","article-title":"SANE 2.0: System for fine grained named entity typing on textual data","volume":"84","author":"Lal","year":"2019","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.3233\/IDA-205720_ref33","doi-asserted-by":"crossref","unstructured":"X. Ling and D.S. Weld, Fine-grained entity recognition, in: AAAI, Vol. 12, 2012, pp. 94\u2013100.","DOI":"10.1609\/aaai.v26i1.8122"},{"key":"10.3233\/IDA-205720_ref34","doi-asserted-by":"crossref","unstructured":"Y. Liu, F. Meng, J. Zhang, J. Xu, Y. Chen and J. Zhou, Gcdt: A global context enhanced deep transition architecture for sequence labeling, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 2431\u20132441.","DOI":"10.18653\/v1\/P19-1233"},{"key":"10.3233\/IDA-205720_ref36","doi-asserted-by":"crossref","unstructured":"G. Luo, X. Huang, C.-Y. Lin and Z. Nie, Joint entity recognition and disambiguation, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 879\u2013888.","DOI":"10.18653\/v1\/D15-1104"},{"key":"10.3233\/IDA-205720_ref38","doi-asserted-by":"crossref","unstructured":"X. Ma and F. Xia, Unsupervised dependency parsing with transferring distribution via parallel guidance and entropy regularization, in: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1, 2014, pp. 1337\u20131348.","DOI":"10.3115\/v1\/P14-1126"},{"key":"10.3233\/IDA-205720_ref39","unstructured":"K. Mai, T.-H. Pham, M.T. Nguyen, T.D. Nguyen, D. Bollegala, R. Sasano and S. Sekine, An empirical study on fine-grained named entity recognition, in: Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 711\u2013722."},{"key":"10.3233\/IDA-205720_ref40","unstructured":"A. McCallum, Efficiently inducing features of conditional random fields, in: Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 2002, pp. 403\u2013410."},{"key":"10.3233\/IDA-205720_ref42","doi-asserted-by":"crossref","unstructured":"D. Nadeau, P.D. Turney and S. Matwin, Unsupervised named-entity recognition: Generating gazetteers and resolving ambiguity, in: Conference of the Canadian Society for Computational Studies of Intelligence, Springer, 2006, pp. 266\u2013277.","DOI":"10.1007\/11766247_23"},{"key":"10.3233\/IDA-205720_ref43","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1162\/tacl_a_00094","article-title":"J-nerd: Joint named entity recognition and disambiguation with rich linguistic features","volume":"4","author":"Nguyen","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.3233\/IDA-205720_ref47","doi-asserted-by":"crossref","unstructured":"L. Ratinov and D. Roth, Design challenges and misconceptions in named entity recognition, in: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, Association for Computational Linguistics, 2009, pp. 147\u2013155.","DOI":"10.3115\/1596374.1596399"},{"key":"10.3233\/IDA-205720_ref48","doi-asserted-by":"crossref","unstructured":"X. Ren, W. He, M. Qu, L. Huang, H. Ji and J. Han, Afet: Automatic fine-grained entity typing by hierarchical partial-label embedding, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016, pp.\u00a01369\u20131378.","DOI":"10.18653\/v1\/D16-1144"},{"key":"10.3233\/IDA-205720_ref49","unstructured":"A. Ritter, S. Clark, O. Etzioni et al., Named entity recognition in tweets: an experimental study, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2011, pp.\u00a01524\u20131534."},{"key":"10.3233\/IDA-205720_ref50","doi-asserted-by":"crossref","unstructured":"K. Sechidis, G. Tsoumakas and I. Vlahavas, On the Stratification of Multi-label Data, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2011, pp. 145\u2013158.","DOI":"10.1007\/978-3-642-23808-6_10"},{"key":"10.3233\/IDA-205720_ref51","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser and I. Polosukhin, Attention is all you need, in: Advances in Neural Information Processing Systems, 2017, pp. 5998\u20136008."},{"key":"10.3233\/IDA-205720_ref53","doi-asserted-by":"crossref","unstructured":"W.Y. Wang and D. Yang, That\u2019s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 2557\u20132563.","DOI":"10.18653\/v1\/D15-1306"},{"key":"10.3233\/IDA-205720_ref54","first-page":"624","article-title":"Named entity recognition in chinese clinical text using deep neural network","volume":"216","author":"Wu","year":"2015","journal-title":"Studies in Health Technology and Informatics"},{"key":"10.3233\/IDA-205720_ref58","doi-asserted-by":"crossref","unstructured":"D. Ye, Z. Xing, C.Y. Foo, Z.Q. Ang, J. Li and N. Kapre, Software-specific named entity recognition in software engineering social content, in: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), IEEE, Vol. 1, 2016, pp. 90\u2013101.","DOI":"10.1109\/SANER.2016.10"},{"key":"10.3233\/IDA-205720_ref59","unstructured":"M.A. Yosef, S. Bauer, J. Hoffart, M. Spaniol and G. 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