This work is licensed under the Creative Commons Attribution 4.0 International License.
Abdelgawad, L., Kluegl, P., Genc, E., Falkner, S., & Hutter, F. (2020). Optimizing Neural Networks for Patent Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). volume 11908 LNAI. doi:10.1007/978-3-030-46133-1{\_}41.AbdelgawadL.KlueglP.GencE.FalknerS.HutterF.2020Optimizing Neural Networks for Patent ClassificationInvolume 11908LNAI10.1007/978-3-030-46133-1{\_}41Open DOISearch in Google Scholar
Aristodemou, L., & Tietze, F. (2018). The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55, 37–51. doi:10.1016/J.WPI.2018.07.002.AristodemouL.TietzeF.2018The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data55375110.1016/J.WPI.2018.07.002Open DOISearch in Google Scholar
Bispo, T.D., Macedo, H.T., Santos, F.D.O., Da Silva, R.P., Matos, L.N., Prado, B.O., Da Silva, G.J., & Guimarães, A. (2019). Long short-term memory model for classification of english-PtBR cross-lingual hate speech. Journal of Computer Science, 15. doi:10.3844/jcssp.2019.1546.1571.BispoT.D.MacedoH.T.SantosF.D.O.Da SilvaR.P.MatosL.N.PradoB.O.Da SilvaG.J.GuimarãesA.2019Long short-term memory model for classification of english-PtBR cross-lingual hate speech1510.3844/jcssp.2019.1546.1571Open DOISearch in Google Scholar
Quinta de Castro, P.V., Félix Felipe da Silva, N., & da Silva Soares, A. (2018). Portuguese Named Entity Recognition Using LSTM-CRF. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). volume 11122 LNAI. doi:10.1007/978-3-319-99722-3{\_}9.Quinta de CastroP.V.Félix Felipe da SilvaN.da Silva SoaresA.2018Portuguese Named Entity Recognition Using LSTM-CRFInvolume 11122LNAI10.1007/978-3-319-99722-3{\_}9Open DOISearch in Google Scholar
De Castro, P.V.Q., Da Silva, N.F.F., & Da Silva Soares, A. (2019). Contextual representations and semi-supervised named entity recognition for Portuguese language. In CEUR Workshop Proceedings. volume 2421.De CastroP.V.Q.Da SilvaN.F.F.Da Silva SoaresA.2019InCEUR Workshop Proceedingsvolume 2421.Search in Google Scholar
Derieux, F., Bobeica, M., Pois, D., & Raysz, J.P. (2010). Combining semantics and statistics for patent classification. In CEUR Workshop Proceedings. volume 1176.DerieuxF.BobeicaM.PoisD.RayszJ.P.2010InCEUR Workshop Proceedingsvolume 1176.Search in Google Scholar
Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL HLT 2019–2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies—Proceedings of the Conference. volume 1.DevlinJ.ChangM.W.LeeK.ToutanovaK.2019InNAACL HLT 2019–2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies—Proceedings of the Conferencevolume 1.Search in Google Scholar
Espacenet (2021). Espacenet Patent search. URL: https://lp.espacenet.com/?locale=pt_LP.Espacenet2021URL: https://lp.espacenet.com/?locale=pt_LP.Search in Google Scholar
Feldman, R., & Sanger, J. (2006). The Text Mining Handbook. doi:10.1017/cbo9780511546914.FeldmanR.SangerJ.200610.1017/cbo9780511546914Open DOISearch in Google Scholar
Gomez, J.C., & Moens, M.F. (2014). A survey of automated hierarchical classification of patents. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8830. doi:10.1007/978-3-319-12511-4.GomezJ.C.MoensM.F.2014A survey of automated hierarchical classification of patents883010.1007/978-3-319-12511-4Open DOISearch in Google Scholar
Gonçalves, T., Silva, C., Quaresma, P., & Vieira, R. (2006). Analysing part-of speech for Portuguese text classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). volume 3878 LNCS.GonçalvesT.SilvaC.QuaresmaP.VieiraR.2006Analysing part-of speech for Portuguese text classificationInvolume 3878LNCS10.1007/11671299_57Search in Google Scholar
Hu, J., Li, S.B., Hu, J.J., & Yang, G.C. (2018). A hierarchical feature extraction model for multi-label mechanical patent classification. Sustainability (Switzerland), 10. doi:10.3390/su10010219.HuJ.LiS.B.HuJ.J.YangG.C.2018A hierarchical feature extraction model for multi-label mechanical patent classification1010.3390/su10010219Open DOISearch in Google Scholar
Instituto Nacional da Propriedade Intelectual (2018). Código da Propriedade Industrial. URL: https://inpi.justica.gov.pt/Portals/6/PDF%20INPI/Legisla%C3%A7%C3%A3o%20e%20outros%20documentos/CPI%20-%202018.pdf?ver=2019-06-28-153157-733.Instituto Nacional da Propriedade Intelectual2018URL: https://inpi.justica.gov.pt/Portals/6/PDF%20INPI/Legisla%C3%A7%C3%A3o%20e%20outros%20documentos/CPI%20-%202018.pdf?ver=2019-06-28-153157-733.Search in Google Scholar
IP5 (2019). IP5 Statistics Report 2018 Edition. URL: https://www.fiveipoffices.org/statistics/statisticsreports/2019editionIP52019URL: https://www.fiveipoffices.org/statistics/statisticsreports/2019editionSearch in Google Scholar
Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. doi:10.3390/info10040150.KowsariK.MeimandiK.J.HeidarysafaM.MenduS.BarnesL.BrownD.201910.3390/info10040150Open DOISearch in Google Scholar
Krestel, R., Chikkamath, R., Hewel, C., & Risch, J. (2021). A survey on deep learning for patent analysis. World Patent Information, 65, 102035.KrestelR.ChikkamathR.HewelC.RischJ.2021A survey on deep learning for patent analysis6510203510.1016/j.wpi.2021.102035Search in Google Scholar
Lai, K., & Wu, S.J. (2005). Using the patent co–citation approach to establish a new patent classification system. Information Processing and Management, 41(2), 313–330LaiK.WuS.J.2005Using the patent co–citation approach to establish a new patent classification system41231333010.1016/j.ipm.2003.11.004Search in Google Scholar
Lee, J.S., & Hsiang, J. (2020). Patent classification by fine-tuning BERT language model. World Patent Information, 61. doi:10.1016/j.wpi.2020.101965.LeeJ.S.HsiangJ.2020Patent classification by fine-tuning BERT language model6110.1016/j.wpi.2020.101965Open DOISearch in Google Scholar
Li, S.B., Hu, J., Cui, Y.X., & Hu, J.J. (2018). DeepPatent: patent classification with convolutional neural networks and word embedding. Scientometrics, 117. doi:10.1007/s11192-018-2905-5.LiS.B.HuJ.CuiY.X.HuJ.J.2018DeepPatent: patent classification with convolutional neural networks and word embedding11710.1007/s11192-018-2905-5Open DOISearch in Google Scholar
Liddy, E.D. (2001). Natural Language Processing. In Encyclopedia of Library and Information Science. Encyclopedia of Library and Information Science.LiddyE.D.2001Natural Language ProcessingInEncyclopedia of Library and Information Science.Search in Google Scholar
Manning, C.D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. doi:10.1017/cbo9780511809071.ManningC.D.RaghavanP.SchutzeH.200810.1017/cbo9780511809071Open DOISearch in Google Scholar
Pan, S.J., & Yang, Q. (2010). A survey on transfer learning. doi:10.1109/TKDE.2009.191.PanS.J.YangQ.201010.1109/TKDE.2009.191Open DOISearch in Google Scholar
Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In NAACL HLT 2018—2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies—Proceedings of the Conference. volume 1. doi:10.18653/v1/n18-1202.PetersM.E.NeumannM.IyyerM.GardnerM.ClarkC.LeeK.ZettlemoyerL.2018InNAACL HLT 2018—2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies—Proceedings of the Conferencevolume 1.10.18653/v1/n18-1202Open DOISearch in Google Scholar
Risch, J., & Krestel, R. (2019). Domain-specific word embeddings for patent classification. Data Technologies and Applications, 53. doi:10.1108/DTA-01-2019-0002.RischJ.KrestelR.2019Domain-specific word embeddings for patent classification5310.1108/DTA-01-2019-0002Open DOISearch in Google Scholar
Rodrigues, R.C., Rodrigues, J., de Castro, P.V.Q., da Silva, N.F.F., & Soares, A. (2020). Portuguese language models and word embeddings: Evaluating on semantic similarity tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). volume 12037 LNAI. doi:10.1007/978-3-030-41505-1{\_}23.RodriguesR.C.RodriguesJ.de CastroP.V.Q.da SilvaN.F.F.SoaresA.2020Portuguese language models and word embeddings: Evaluating on semantic similarity tasksInvolume 12037LNAI10.1007/978-3-030-41505-1{\_}23Open DOISearch in Google Scholar
dos Santos, C., & Guimarães, V. (2015). Boosting Named Entity Recognition with Neural Character Embeddings. doi:10.18653/v1/w15-3904.dos SantosC.GuimarãesV.201510.18653/v1/w15-3904Open DOISearch in Google Scholar
Silva, C., & Ribeiro, B. (2010). Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques. volume 255. doi:10.1007/978-3-642-04533-2.SilvaC.RibeiroB.2010volume 255.10.1007/978-3-642-04533-2Open DOISearch in Google Scholar
Souza, F., Nogueira, R., & Lotufo, R. (2019). Portuguese Named Entity Recognition using BERT-CRF. arXiv. URL: https://arxiv.org/abs/1909.10649v2.SouzaF.NogueiraR.LotufoR.2019Portuguese Named Entity Recognition using BERT-CRFURL: https://arxiv.org/abs/1909.10649v2.Search in Google Scholar
Trappey, A.J., Hsu, F C., Trappey, C.V., & Lin, C.I. (2006). Development of a patent document classification and search platform using a back-propagation network. Expert Systems with Applications, 31. doi:10.1016/j.eswa.2006.01.013.TrappeyA.J.HsuF C.TrappeyC.V.LinC.I.2006Development of a patent document classification and search platform using a back-propagation network3110.1016/j.eswa.2006.01.013Open DOISearch in Google Scholar
Trappey, A.J., Trappey, C.V., Chiang, T.A., & Huang, Y.H. (2013). Ontology-based neural network for patent knowledge management in design collaboration. International Journal of Production Research, 51. doi:10.1080/00207543.2012.701775.TrappeyA.J.TrappeyC.V.ChiangT.A.HuangY.H.2013Ontology-based neural network for patent knowledge management in design collaboration5110.1080/00207543.2012.701775Open DOISearch in Google Scholar
Trappey, A.J.C., Trappey, C.V., Wu, C.-Y., & Lin, C.-W. (2012). A patent quality analysis for innovative technology and product development. Advanced Engineering Informatics, 26, 26–34. doi:10.1016/j.aei.2011.06.005.TrappeyA.J.C.TrappeyC.V.WuC.-Y.LinC.-W.2012A patent quality analysis for innovative technology and product development26263410.1016/j.aei.2011.06.005Open DOISearch in Google Scholar
Wagner Filho, J.A., Wilkens, R., Idiart, M., & Villavicencio, A. (2019). The BRWAC corpus: A new open resource for Brazilian Portuguese. In LREC 2018—11th International Conference on Language Resources and Evaluation.Wagner FilhoJ.A.WilkensR.IdiartM.VillavicencioA.2019InLREC 2018—11th International Conference on Language Resources and EvaluationSearch in Google Scholar
World Intellectual Property Organization (2008). WIPO Intellectual Property Handbook: Policy, Law and Use. doi:1.World Intellectual Property Organization2008doi:1.Search in Google Scholar
Wu, J.L., Chang, P.C., Tsao, C.C., & Fan, C.Y. (2016). A patent quality analysis and classification system using self-organizing maps with support vector machine. Applied Soft Computing Journal, 41. doi:10.1016/j.asoc.2016.01.020.WuJ.L.ChangP.C.TsaoC.C.FanC.Y.2016A patent quality analysis and classification system using self-organizing maps with support vector machine4110.1016/j.asoc.2016.01.020Open DOISearch in Google Scholar
Zhang, X.Y. (2014). Interactive patent classification based on multi-classifier fusion and active learning. Neurocomputing, 127. doi:10.1016/j.neucom.2013.08.013.ZhangX.Y.2014Interactive patent classification based on multi-classifier fusion and active learning12710.1016/j.neucom.2013.08.013Open DOISearch in Google Scholar
Zhuang, F.Z., Qi, Z.Y., Duan, K.Y., Xi, D.B., Zhu, Y.C., Zhu, H.S., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning, in Proceedings of the IEEE, 109(1), Jan. 2021. doi:10.1109/JPROC.2020.3004555.ZhuangF.Z.QiZ.Y.DuanK.Y.XiD.B.ZhuY.C.ZhuH.S.XiongH.HeQ.2021A comprehensive survey on transfer learningin1091Jan.202110.1109/JPROC.2020.3004555Open DOISearch in Google Scholar