论文标题
用ARTLM绘制当代艺术世界：一种特定于艺术的NLP模型
Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
论文作者
论文摘要
随着艺术界越来越多的数据，发现适合收藏家口味的艺术家和艺术品成为一个挑战。使用视觉信息已不再足够了，因为有关艺术家的上下文信息在当代艺术中也同样重要。在这项工作中，我们提出了一个通用的自然语言处理框架（称为ARTLM），以根据他们的传记来发现当代艺术家之间的联系。在这种方法中，我们首先继续使用大量与无标记的艺术相关数据预先培训现有的通用英语模型。然后，我们通过由艺术行业的专业人员团队手动注释的传记对数据集对这种新的预培训模型进行了微调。通过广泛的实验，我们证明了我们的ARTLM可以达到85.6％的精度和84.0％的F1得分，并且表现优于其他基线模型。我们还提供了根据ARTLM输出构建的艺术家网络的可视化和定性分析。
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.
