Robust Named Entity Recognition and Linking on Historical Multilingual Documents

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the Identifying Historical People, Places, and other Entities (HIPE) evaluation campaign of CLEF 2020. Our participation relies on two neural models, one for named entity recognition and classification (NERC) and another one for entity linking (EL). We carefully pre-processed inputs to mitigate its flaws, notably in terms of segmentation. Our submitted runs cover all languages (English, French, and German) and sub-tasks proposed in the lab: NERC, end- to-end EL, and EL-only. Our submissions obtained top performance in 50 out of the 52 scoreboards proposed by the lab organizers. In further detail, out of 70 runs submitted by 13 participants, our approaches obtained the best score for all metrics in all three languages both for NERC and for end-to-end EL. It also obtained the best score for all metrics in French and German for EL-only.