In part I and part II of this series, we checked the effectiveness of applying automated named entity recognition on a set of miscellaneous documents in a corporate document management system. The idea is to use machine learning and artificial intelligence techniques to identify and extract natural person names from documents. This results in a list of person names and the position of where those person names appear in the document, and that information can be added to the document as meta-data. This can e.g., be used to link data or to derive GDPR-sensitivity.
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Topics:
Alfred,
Machine Learning,
Artificial Intelligence,
data recognition,
spaCy,
python,
openNLP,
bagging,
boosting,
ensemble
In this pilot study, Tom Magerman - MSc Business and Information Systems Engineering, PhD at UCLL (University Colleges Leuven Limburg) - and our team at Xenit, want to check the effectiveness of applying automated named entity recognition on a set of miscellaneous documents in a corporate document management system. The idea is to use machine learning (artificial intelligence) techniques to identify and extract natural person names from documents.
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Topics:
GDPR,
Machine Learning,
Artificial Intelligence,
data recognition
The Association of Intelligent Information Management (AIIM) published survey results from 100 Leaders in Document Automation, focusing on how the digital workforce takes advantage of Document Automation Solutions.
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Topics:
Alfresco,
Cloud,
ECM,
AIIM,
Machine Learning,
Artificial Intelligence
