Titre :

[Données] Le jeu de données ATLANTIS

Auteur(s) :

Rawsthorne, Helen Mair [Auteur]
Laboratoire sciences et technologies de l'information géographique [LaSTIG]
Abadie, Nathalie [Auteur]
Laboratoire sciences et technologies de l'information géographique [LaSTIG]
Kergosien, Eric [Auteur]
Groupe d'Études et de Recherche Interdisciplinaire en Information et COmmunication - ULR 4073 [GERIICO ]
Duchêne, Cécile [Auteur]
Laboratoire sciences et technologies de l'information géographique [LaSTIG]
Saux, Éric [Auteur]
Institut de Recherche de l'Ecole Navale [IRENAV]

Éditeur :

Zenodo

Date de publication :

2023-11-13

Mot(s)-clé(s) :

Reconnaissance d'entités nommées spatiales imbriquées
Reconnaissance de relations spatiales
Entraînement de modèles de langue

Mot(s)-clé(s) en anglais :

Nested Spatial NER
Spatial relations recognition
Language model training

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

ATLANTIS is a French-language dataset for nested spatial entity and binary spatial relation extraction from text. The dataset is composed of extracts from 15 different volumes of the Instructions nautiques. The Instructions ...
Lire la suite >ATLANTIS is a French-language dataset for nested spatial entity and binary spatial relation extraction from text. The dataset is composed of extracts from 15 different volumes of the Instructions nautiques. The Instructions nautiques are a series of books produced and published by the French Naval Hydrographic and Oceanographic Service, the Shom. Each volume contains essential information for navigating safely in the coastal waters of a specific geographic area, including instructions for entering ports and descriptions of the coastal maritime environment. We extracted the text from the PDF documents using pdfminer.six. The volumes, which are written in French, cover coastal areas in Africa, Europe, North and South America, as well as in the Indian and Pacific Oceans.The TextMine'24 Dataset is a subset of the ATLANTIS Dataset and contains nested spatial entity annotations. It was used as the benchmark dataset for the Défi TextMine 2024, a spatial entity recognition challenge hosted by the TextMine working group which is part of EGC, the International Francophone Association for Knowledge Extraction and Management.This work is co-financed by the Shom and the IGN and is being carried out at the LASTIG, a research unit at Université Gustave Eiffel.Under no circumstances should any part of this work be used for real-life navigation.Lire moins >

Langue :

Français

Vulgarisation :

Non

Collections :

• Groupement d'Études et de Recherche Interdisciplinaire en Information et Communication (GERiiCO) - ULR 4073

Source :

Harvested from HAL