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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