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Tytuł artykułu

Robotic process automation of unstructured data with machine learning

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (09-12.09.2018 ; Poznań, Poland)
Języki publikacji
EN
Abstrakty
EN
In this paper we present our work in progress on building an artificial intelligence system dedicated to tasks regarding the processing of formal documents used in various kinds of business procedures. The main challenge is to build machine learning (ML) models to improve the quality and efficiency of business processes involving image processing, optical character recognition (OCR), text mining and information extraction. In the paper we introduce the research and application field, some common techniques used in this area and our preliminary results and conclusions.
Rocznik
Tom
Strony
9--16
Opis fizyczny
Bibliogr. 33 poz., rys., wz., tab.
Twórcy
  • Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, Warszawa, Poland
  • Applica.ai, ul. Wiślana 8, Warszawa, Poland
  • Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, Warszawa, Poland
  • Applica.ai, ul. Wiślana 8, Warszawa, Poland
  • Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, Warszawa, Poland
  • Applica.ai, ul. Wiślana 8, Warszawa, Poland
  • Applica.ai, ul. Wiślana 8, Warszawa, Poland
Bibliografia
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  • [3] Capgemini Consulting, “Robotic Process Automation – Robots conquer business processes in back offices,” 2016, accessed: 2018-06-07. [Online]. Available: https://www.capgemini.com/consulting-de/wp-content/uploads/sites/32/2017/08/robotic-process-automation-study.pdf
  • [4] PWC, “Rethinking retail: Artificial Intelligence and Robotic Process Automation,” 2017, accessed: 2018-06-07. [Online]. Available: https://www.pwc.be/en/documents/20171123-rethinking-retail-artificial-intelligence-and-robotic-process-automation.pdf
  • [5] David Schatsky and Craig Muraskin and Kaushik Iyengar, “Robotic process automation A path to the cognitive enterprise,” 2016, accessed: 2018-06-07. [Online]. Available: https://www2.deloitte.com/content/dam/insights/us/articles/3451_Signals_Robotic-process-automation/DUP_Signals_Robotic-process-automation.pdf
  • [6] S. Aguirre and A. Rodriguez, “Automation of a business process using robotic process automation (rpa): A case study,” in Workshop on Engineering Applications. Springer, 2017, pp. 65–71.
  • [7] H. P. Fung, “Criteria, use cases and effects of information technology process automation (itpa),” 07 2014.
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  • [14] ImageNet dataset official website, “Imagenet,” 2018, [Online; accessed 7-June-2018]. [Online]. Available: http://www.image-net.org
  • [15] COCO dataset official website, “Coco,” 2018, [Online; accessed 7-June-2018]. [Online]. Available: http://cocodataset.org
  • [16] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
  • [17] X. Li, “Classification with large sparse datasets: Convergence analysis and scalable algorithms,” 2017.
  • [18] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871–1874, Jun. 2008. [Online]. Available: http://dl.acm.org/citation.cfm?id=1390681.1442794
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  • [26] S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” CoRR, vol. abs/1803.01271, 2018. [Online]. Available: http://arxiv.org/abs/1803.01271
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  • [28] J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of the Eighteenth International Conference on Machine Learning, ser. ICML ’01. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2001, pp. 282–289. [Online]. Available: http://dl.acm.org/citation.cfm?id=645530.655813
  • [29] H. M. Wallach, “Conditional random fields: An introduction,” Technical Reports (CIS), p. 22, 2004.
  • [30] J. P. C. Chiu and E. Nichols, “Named entity recognition with bidirectional lstm-cnns,” CoRR, vol. abs/1511.08308, 2015. [Online]. Available: http://arxiv.org/abs/1511.08308
  • [31] Z. Yang, R. Salakhutdinov, and W. W. Cohen, “Multi-task cross-lingual sequence tagging from scratch,” CoRR, vol. abs/1603.06270, 2016. [Online]. Available: http://arxiv.org/abs/1603.06270
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Uwagi
1. Track: Preface
2. Technical Session: 13th International Symposium Advances in Artificial Intelligence and Applications
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a617562e-2f8d-4c90-acb9-343100fed6c4
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