Cartolabe: A Web-Based Scalable Visualization of Large Document Collections

Philippe Caillou (1) , Jonas Renault (2) , Jean-Daniel Fekete (3) , Anne-Catherine Letournel (1, 4) , Michèle Sebag (1)

1 TAU - TAckling the Underspecified
2 LISN - Laboratoire Interdisciplinaire des Sciences du Numérique
3 AVIZ - Analysis and Visualization
4 ASARD - Accompagnement et Soutien aux Activités de Recherche & Développement - LISN

We describe CARTOLABE, a web-based multi-scale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, CARTOLABE has evolved to become a generic system and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). CARTOLABE is made of two modules: the first relies on Natural Language Processing methods, converting a corpus and its entities (documents, authors, concepts) into high-dimensional vectors, computing their projection on the 2D plane, and extracting meaningful labels for regions of the plane. The second module is a Web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the UMAP projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning and zooming on regions of semantic interest. Three use cases are discussed to illustrate CARTOLABE versatility and ability to bring large scale textual corpus visualization and exploration to a wide audience.

Domaines: Interface homme-machine [cs.HC]; Intelligence artificielle [cs.AI]

Philippe Caillou, Jonas Renault, Jean-Daniel Fekete, Anne-Catherine Letournel, Michèle Sebag. Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. IEEE Computer Graphics and Applications, 2021, 41 (2), pp.76--88. ⟨10.1109/MCG.2020.3033401⟩. ⟨hal-02499006⟩