Abstract
The text mining process typically involves the application of natural language processing (NLP) techniques, in order to obtain important information and extract insights from texts. This is achieved by detecting patterns, which are not explicitly a priori in this unstructured or semi-structured dataset. One of the most significant NLP tasks is Named Entity Recognition (NER). The NER process seeks to extract and classify mentioned entities detected in a text written in natural language. These categories are predefined and can be names of people or organizations, locations, dates, monetary values, specific codes, etc. A wide range of algorithms based on LSTM (Long-Short Term Memory) architecture has being proposed to enhance the NER accuracy. However, a key component to a successful information extraction is the corpora used for NER training. Another key issue concerns the language being worked on, since the vast majority of algorithms were designed to work with English. According to the literature, while the NER process applied to the English language reaches about 90% accuracy, when it is applied to the Portuguese language, this precision reaches a maximum of 83.38%. This paper proposes a methodology to improve the Portuguese-based NER, which uses journalistic corpora as a basis for text corpora training. We believe the journalistic writting has the best adherence to the contemporaneity of any language, since it preserves features such as objectivity, simplicity, impartiality, and is a reference of transmitting the information without ambiguity. The proposed methodology provides a model to extract entities and assess the obtained results with the use of Recurrent Neural Network architectures. At the best of our knowledge, the proposed methodology applied to the Portuguese language not only overcomes the average accuracy found in the literature by increasing it from 83.38% to 85.64%, but also could decrease the computational costs related to the NER processing tasks.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdelali A, Cowie J, Soliman H (2005) Building a modern standard arabic corpus. In: Workshop on computational modeling of lexical acquisition 25–28
do Amaral DOF, Vieira R (2014) Nerp-crf: uma ferramenta para o reconhecimento de entidades nomeadas por meio de conditional random fields. Linguamática 6(1):41–49
Amri S, Zenkouar L, Outahajala M (2017) Build a morphosyntaxically annotated amazigh corpus. In: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications 1–7
de Araujo PHL, de Campos TE, de Oliveira RR, Stauffer M, Couto S, Bermejo P (2018) Lener-br: A dataset for named entity recognition in brazilian legal text. In: International Conference on Computational Processing of the Portuguese Language 313–323. Springer
Cavalin P, Figueiredo F, de Bayser M, Moyano L, Candello H, Appel A, Souza R (2016) Building a question-answering corpus using social media and news articles. In: International Conference on Computational Processing of the Portuguese Language 353–358. Springer
da Consolação Dias, C (2015) A análise de domínio, as comunidades discursivas, a garantia de literatura e outras garantias. Informação & Sociedade 25(2)
Holthaus P, Leichsenring C, Bernotat J, Richter V, Pohling M, Carlmeyer B, Köster N, zu Borgsen SM, Zorn R, Schiffhauer B et al. (2016) How to address smart homes with a social robot? a multi-modal corpus of user interactions with an intelligent environment. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 3440–3446
Mooers CN (1960) The next twenty years in information retrieval; some goals and predictions. Am Doc 11(3):229–236
Mosley M, Brackett MH, Earley S, Henderson D (2010) DAMA guide to the data management body of knowledge. Technics Publications
Nelson DMQ (2017) Uso de redes neurais recorrentes para previsão de séries temporais ffinanceiras
de Oliveira MG, de Souza Baptista C, Campelo CE, Bertolotto M (2017) A goldstandard social media corpus for urban issues. In: Proceedings of the Symposium on Applied Computing 1011–1016
Pirovani J, Oliveira E (2018) Portuguese named entity recognition using conditional random fields and local grammars. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
PNAD I (2017) Acesso à internet e à televisão e posse de telefone móvel celular para uso pessoal 2017. Rio de Janeiro: IBGE (Instituto Brasileiro de Geografa e Estatística)
Pretto JR (2009) O estilo jornalístico. Estudos Linguísticos 38(3):481–491
Santos J, Consoli B, dos Santos C, Terra J, Collonini S, Vieira R (2019) Assessing the impact of contextual embeddings for portuguese named entity recognition. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 437–442. IEEE
de Aquino Silva R, da Silva L, Dutra ML, de Araujo GM (2020) A new entity extraction model based on journalistic Brazilian Portuguese language to enhance named entity recognition. In: Mugnaini R (eds) Data and information in online environments. DIONE 2020. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-50072-6_5
Souza F, Nogueira R, Lotufo R (2019) Portuguese named entity recognition using bert-crf. arXiv preprint arXiv:1909.10649
Spoustová J, Spousta M, Pecina P (2010) Building a web corpus of czech
Villalva A, Mateus MHM (2008) Morfologia do português. Universidade Aberta Lisboa
Yan S (2015) Understanding lstm networks. Online). Accessed on August 11
Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) 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), 207–212
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
de Aquino Silva, R., da Silva, L., Dutra, M.L. et al. An Improved NER Methodology to the Portuguese Language. Mobile Netw Appl 26, 319–325 (2021). https://doi.org/10.1007/s11036-020-01644-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-020-01644-x