  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.
