Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
This addresses the problem for art collectors and professionals by providing a tool to navigate art data, though it is incremental as it adapts existing NLP methods to a new domain.
The paper tackles the challenge of discovering artists and artworks in the contemporary art world by developing ArtLM, an art-specific NLP model that analyzes artist biographies to uncover connections, achieving 85.6% accuracy and 84.0% F1 score in experiments.
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.