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Research paper

Augmenting the Metadata of Audiovisual Archives with NLP Techniques: Challenges and Solutions

This paper sets out to highlight and address the challenges in working with large audiovisual archives such as the Radio Télévision Suisse collection. The goal of this research is to augment the metadata through Natural Language Processing methods to facilitate access to the archive for the larger public.

Alliata, Giacomo, Yang, Yuchen, Kenderdine, Sarah
Creative Commons Attribution 4.0 InternationalView resource
Research paper

DEEP LEARNING PARADIGMS IN CULTURAL ANALYTICS: BRIDGING ARTIFICIAL INTELLIGENCE AND SOCIETAL TRANSFORMATION

The fast growth of digitized museum collections has generated novel methodological requirements of cultural analytics, especially automated organization and interpretation of massive cultural heritage metadata. This paper will analyze how AI-based cultural analytics can be aided with the help of deep learning, creating a supervised metadata classification model based on the Metropolitan Museum of Art Open Access Dataset. The dataset includes a big collection of records of cultural objects with institutional, textual, material, chronological, artist-associated and cultural descriptors. A Deep Cultural Metadata Classifier was designed to predic

Shivani Tanwar, Vishalkumar Parmar, Jayesh Kumar, Deepa Nehra, Dr. Pubali Mitra Paul
Creative Commons Attribution 4.0 InternationalView resource

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