Nathalie Abadie

Création d’un graphe de connaissances géohistorique à partir d’annuaires du commerce parisien du 19 ème siècle: Application aux métiers de la photographie

By Solenn Tual, Nathalie Abadie, Bertrand Duménieu, Jospeh Chazalon, Edwin Carlinet

2023-07-01

In 34es Journées francophones d’Ingénierie des Connaissances (IC 2023) @ Plate-Forme Intelligence Artificielle (PFIA 2023)

Abstract Les annuaires professionnels anciens, édités à un rythme soutenu dans de nombreuses villes européennes tout au long des XIXe et XXe si‘ecles, forment un corpus de sources unique par son volume et la possibilité qu’ils donnent de suivre les transformations urbaines à travers le prisme des activités professionnelles des habitants, de l’échelle individuelle jusqu’à celle de la ville enti‘ere. L’analyse spatiotemporelle d’un type de commerces au travers des entrées d’annuaires demande cependant un travail considérable de recensement, de transcription et de recoupement manuels.

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A benchmark of nested named entity recognition approaches in historical structured documents

By Solenn Tual, Nathalie Abadie, Joseph Chazalon, Bertrand Duménieu, Edwin Carlinet

2023-06-01

In Proceedings of the international conference on document analysis and recognition (ICDAR 2023)

Abstract Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities are often nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based approach based on joint labelling and semantic weighting of errors, evaluated on a collection of 19th-century Paris trade directories.

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A benchmark of named entity recognition approaches in historical documents

By Nathalie Abadie, Edwin Carlinet, Joseph Chazalon, Bertrand Duménieu

2022-04-07

In Proceedings of the 15th IAPR international workshop on document analysis system

Abstract Named entity recognition (NER) is a necessary step in many pipelines targeting historical documents. Indeed, such natural language processing techniques identify which class each text token belongs to, e.g. “person name”, “location”, “number”. Introducing a new public dataset built from 19th century French directories, we first assess how noisy modern, off-the-shelf OCR are. Then, we compare modern CNN- and Transformer-based NER techniques which can be reasonably used in the context of historical document analysis.

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