After decades of massive digitisation, an unprecedented number of historical documents are
available in digital format, along with their machine-readable texts. While this represents a …

Large language models (LLMs) have been leveraged for several years now, obtaining state-
of-the-art performance in recognizing entities from modern documents. For the last few …

The advancement of machine learning and symbolic approaches have underscored their
strengths and weaknesses in Natural Language Processing (NLP). While machine learning …

In this paper, we address the detection of named entities in multilingual historical collections.
We argue that, besides the multiple challenges that depend on the quality of digitization (eg …

In this paper, we examine the application of toponym linking to digitised historical
newspapers. These collections constitute the largest trove of historical text data available to …

In this article, we introduce an Open Education Resource (OER) on digital historical
research with historical newspapers, 1 intended to give students the means to understand …

Les campagnes actuelles de numérisation de documents historiques issus de fonds
documentaires du monde entier ouvrent de nouvelles voies aux historiens, historiennes et …

Les modèles de langage de grande taille (LLM) sont exploités depuis plusieurs années
maintenant, obtenant des performances de pointe dans la reconnaissance d'entités à partir …

Les modèles de langage de grande taille (LLM) sont exploités depuis plusieurs années
maintenant, obtenant des performances de l'état de l'art dans la reconnaissance d'entités à …

Large language models (LLMs) have been leveraged for several years now, obtaining state-
of-the-art performance in recognizing entities from modern documents. For the last few …