Transdisciplinary analysis of a corpus of french newsreels: The ANTRACT project

Carrive, Jean; Beloued, Abdelkrim; Goetschel, Pascale; Heiden, Serge; Laurent, Antoine; Lisena, Pasquale; Mazuet, Franck; Meignier, Sylvain; Pincemin, Bénédicte; Poels, Géraldine; Troncy, Raphaël
Digital Humanities Quarterly, Alliance of Digital Humanities, March 2021, Special Issue on AudioVisual Data in DH, 15 (1)

The ANTRACT project is a cross-disciplinary apparatus dedicated to the analysis of the French newsreel company Les Actualités Françaises (1945-1969) and its film productions. Founded during the liberation of France, this state-owned company filmed more than 20,000 news reports shown in French cinemas and throughout the world over its 24 years of activity. The project brings together research organizations with a dual historical and technological perspective. ANTRACT’s goal is to study the production process, the film content, the way historical events are represented and the audience reception of Les Actualités Françaises newsreels using innovative AI-based data processing tools developed by partners specialized in image, audio, and text analysis. This article focuses on the data processing apparatus and tools of the project. Automatic content analysis is used to select data, to segment video units and typescript images, and to align them with their archival description. Automatic speech recognition provides a textual representation and natural language processing can extract named entities from the voice-over recording; automatic visual analysis is applied to detect and recognize faces of well-known characters in videos. These multifaceted data can then be queried and explored with the TXM text-mining platform. The results of these automatic analysis processes are feeding the Okapi platform, a client-server software that integrates documentation, information retrieval, and hypermedia capabilities within a single environment based on the Semantic Web standards. The complete corpus of Les Actualités Françaises, enriched with data and metadata, will be made available to the scientific community by the end of the project.
 
 

HAL
Type:
Journal
Date:
2021-03-10
Department:
Data Science
Eurecom Ref:
6489

PERMALINK : https://www.eurecom.fr/publication/6489