AREEj: Arabic Relation Extraction with Evidence

AbstractRelational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and a type. In this paper, we consider Arabic text and introduce evidence enrichment which intuitively informs models for better predictions. Relational evidence is an expression in the text that explains how sources and targets relate. This paper augments the existing SREDFM relational extraction dataset with evidence annotation to its 2.9-million Arabic relations. We leverage the augmented dataset to build AREEj, a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target AREEj outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).