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Authors: Lars Klöser 1 ; Philipp Kohl 1 ; Bodo Kraft 1 and Albert Zündorf 2

Affiliations: 1 FH Aachen, University of Applied Sciences, Germany ; 2 University of Kassel, Germany

Keyword(s): Natural Language Processing, Natural Language Understanding, Information Extraction, Relation Extraction, Joint Relation Extraction, Event Extraction.

Abstract: Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.

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Paper citation in several formats:
Klöser, L.; Kohl, P.; Kraft, B. and Zündorf, A. (2021). Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-526-5; ISSN 2184-9277, SciTePress, pages 148-156. DOI: 10.5220/0010559201480156

@conference{delta21,
author={Lars Klöser. and Philipp Kohl. and Bodo Kraft. and Albert Zündorf.},
title={Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA},
year={2021},
pages={148-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010559201480156},
isbn={978-989-758-526-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
TI - Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction
SN - 978-989-758-526-5
IS - 2184-9277
AU - Klöser, L.
AU - Kohl, P.
AU - Kraft, B.
AU - Zündorf, A.
PY - 2021
SP - 148
EP - 156
DO - 10.5220/0010559201480156
PB - SciTePress

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