Abstract
Knowledge Graphs (KG) offer easy-to-process information. An important issue to build a KG from texts is the Relation Extraction (RE) task that identifies and labels relationships between entity mentions. In this paper, to address the RE problem, we propose to combine a deep learning approach for relation detection, and a symbolic method for relation classification. It allows to have at the same time the performance of deep learning methods and the interpretability of symbolic methods. This method has been evaluated and compared with state-of-the-art methods on TACRED, a relation extraction benchmark, and has shown interesting quantitative and qualitative results.
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Notes
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We decided to use the Stanford CoreNLP toolkit [15].
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We used WordNet [18] to do so.
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Accessible here: https://gitlab.inria.fr/hayats/conceptualknn-relex.
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Ayats, H., Cellier, P., Ferré, S. (2022). A Two-Step Approach for Explainable Relation Extraction. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_2
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