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Authors: Juris Rāts 1 ; Inguna Pede 1 ; Tatjana Rubina 2 and Gatis Vītols 2

Affiliations: 1 RIX Technologies, Blaumana 5a-3, Riga, LV-1011, Latvia ; 2 Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela str., Jelgava, LV-3001, Latvia

ISBN: 978-989-758-423-7

Keyword(s): Machine Learning, Document Classification, Enterprise Content Management, Python, Elasticsearch.

Abstract: The aim of the research is to create and evaluate a flexible model for document capturing that would employ machine learning to classify documents feeding them with values for one or more metadata items. Documents and classification metadata fields typical for Enterprise Content Management (ECM) systems are used in the research. The model comprises selection of classification methods, configuration of the methods hyperparameters and configuration of a number of other learning related parameters. The model provides user with visual means to analyse the classification outcomes and those to tune the further steps of the learning. A couple of challenges are addressed along the way – as informal and eventually changing criteria for document classification, and imbalanced data sets.

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Paper citation in several formats:
Rāts, J.; Pede, I.; Rubina, T. and Vītols, G. (2020). A Flexible Model for Enterprise Document Capturing Automation.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 297-304. DOI: 10.5220/0009034802970304

@conference{iceis20,
author={Juris Rāts. and Inguna Pede. and Tatjana Rubina. and Gatis Vītols.},
title={A Flexible Model for Enterprise Document Capturing Automation},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={297-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009034802970304},
isbn={978-989-758-423-7},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Flexible Model for Enterprise Document Capturing Automation
SN - 978-989-758-423-7
AU - Rāts, J.
AU - Pede, I.
AU - Rubina, T.
AU - Vītols, G.
PY - 2020
SP - 297
EP - 304
DO - 10.5220/0009034802970304

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