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Authors: Sakher Alqaaidi ; Elika Bozorgi and Krzysztof Kochut

Affiliation: School of Computing, University of Georgia, U.S.A.

Keyword(s): Text Mining, Knowledge Graphs, Natural Language Processing, Relation Classification.

Abstract: The relation classification task assigns the proper semantic relation to a pair of subject and object entities; the task plays a crucial role in various text mining applications, such as knowledge graph construction and entities interaction discovery in biomedical text. Current relation classification models employ additional procedures to identify multiple relations in a single sentence. Furthermore, they overlook the imbalanced predictions pattern. The pattern arises from the presence of a few valid relations that need positive labeling in a relatively large predefined relations set. We propose a multiple relations classification model that tackles these issues through a customized output architecture and by exploiting additional input features. Our findings suggest that handling the imbalanced predictions leads to significant improvements, even on a modest training design. The results demonstrate superiority performance on benchmark datasets commonly used in relation classificatio n. To the best of our knowledge, this work is the first that recognizes the imbalanced predictions within the relation classification task. (More)

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Paper citation in several formats:
Alqaaidi, S., Bozorgi, E. and Kochut, K. (2024). Multiple Relations Classification Using Imbalanced Predictions Adaptation. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 511-519. DOI: 10.5220/0012455100003636

@conference{icaart24,
author={Sakher Alqaaidi and Elika Bozorgi and Krzysztof Kochut},
title={Multiple Relations Classification Using Imbalanced Predictions Adaptation},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={511-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012455100003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Multiple Relations Classification Using Imbalanced Predictions Adaptation
SN - 978-989-758-680-4
IS - 2184-433X
AU - Alqaaidi, S.
AU - Bozorgi, E.
AU - Kochut, K.
PY - 2024
SP - 511
EP - 519
DO - 10.5220/0012455100003636
PB - SciTePress

- Science and Technology Publications, Lda.
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