Towards mapping the contemporary art world with ArtLM: an art-specific NLP model

• URL: http://arxiv.org/abs/2212.07127v3
• Date: Fri, 16 Dec 2022 12:52:19 GMT
• Title: Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
• Authors: Qinkai Chen, Mohamed El-Mennaoui, Antoine Fosset, Amine Rebei, Haoyang Cao, Philine Bouscasse, Christy E\'oin O'Beirne, Sasha Shevchenko and Mathieu Rosenbaum
• Abstract summary: We present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge. It is no longer enough to use visual information, as contextual information about the artist has become just as important in contemporary art. In this work, we present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. In this approach, we first continue to pre-train the existing general English language models with a large amount of unlabelled art-related data. We then fine-tune this new pre-trained model with our biography pair dataset manually annotated by a team of professionals in the art industry. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score and outperforms other baseline models. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.

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