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With an increasing amount of data in the art world, discovering artists andartworks suitable to collectors' tastes becomes a challenge. It is no longerenough to use visual information, as contextual information about the artisthas become just as important in contemporary art. In this work, we present ageneric Natural Language Processing framework (called ArtLM) to discover theconnections among contemporary artists based on their biographies. In thisapproach, we first continue to pre-train the existing general English languagemodels with a large amount of unlabelled art-related data. We then fine-tunethis new pre-trained model with our biography pair dataset manually annotatedby a team of professionals in the art industry. With extensive experiments, wedemonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score andoutperforms other baseline models. We also provide a visualisation and aqualitative analysis of the artist network built from ArtLM's outputs.

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