L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

Abstract

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the highresource languages and lower than average for low-resource and multilingual models.

An OER on digital historical research on European historical newspapers with the NewsEye platform

“…articles and graphical regions) from digitized newspapers and transforming the content to textual format, providing full articles through automatic layout analysis, text recognition and article separation. -Semantic Text Enrichment, enhancing the utility of the newspaper collections by enriching the texts with higher-level semantic annotation using named entity recognition (NER) (Boros et al, 2022;Boros et al, 2020). Extracted named entities were linked to external references (such as the Wikipedia) across languages (Pontes et al, 2022;Pontes et al, 2019), with the goal to provide support for multilingual analysis and enable event detection through the discovery of patterns in textual content.…”