Advance computational methods for extracting, classifying and linking information from art-historical texts.

用于从艺术历史文本中提取、分类和链接信息的先进计算方法。

• 批准号: 2775848
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2775848
• 关键词: Advance; computational; methods; extracting; classifying

Much art-historical knowledge is contained in unstructured texts such as catalogues and technical reports. Historically, this information has rarely been converted to structured forms and stored in databases, making it difficult to find information needed for research, which may require complex querying (e.g. paintings which use a mixture of lead white and azurite bound in egg tempera; paintings which were in Paris during Rubens' visit in 1625), or to present the information to the public through innovative and engaging interfaces such as maps, timelines, etc. The project is embedded within the National Gallery's exciting Digital Dossiers Project, a cornerstone of its 2024 Bicentenary celebrations.Research to advance computational methods for extracting and linking information from art-historical texts, focusing on a corpus of National Gallery publications. While NLP techniques in this area have evolved considerably with the advent of modern deep learning methods, they are typically either too general or highly specific, and thus not well adapted to the specific vocabularies and literary conventions in this domain. The research will advance the state of the art in developing novel methods and tools to perform entity recognition, classification and linking specifically for this domain.The work will be incorporated into the Gallery's software tools as a practical project outcome, thereby ensuring high impact as these will be available for widespread use in a public setting. The Gallery has incredibly rich documentation about its paintings, going back for well over a century and a half. Your research will enable them to effectively index new aspects of the collection and present it to the public in new and engaging ways.

许多艺术史知识包含在非结构化的文本中，如目录和技术报告。从历史上看，这些信息很少被转换成结构化形式并存储在数据库中，因此很难找到研究所需的信息，这可能需要复杂的查询（例如以铅白色和蓝铜矿的混合物结合在蛋彩画中的绘画;鲁本斯1625年访问巴黎时的画作），或通过创新和引人入胜的界面（如地图、时间轴等）向公众展示信息。该项目嵌入了国家美术馆令人兴奋的数字档案项目，2024年两百周年庆典的基石。研究推进从艺术史文本中提取和链接信息的计算方法，重点是国家美术馆出版物的语料库。虽然随着现代深度学习方法的出现，这一领域的NLP技术已经有了很大的发展，但它们通常要么过于笼统，要么非常具体，因此不能很好地适应这一领域的特定词汇和文学惯例。该研究将推进开发新方法和工具的最新技术水平，以执行实体识别，分类和链接专门针对这一领域。这项工作将被纳入画廊的软件工具作为一个实际的项目成果，从而确保高影响力，因为这些将在公共环境中广泛使用。画廊有令人难以置信的丰富的文件，关于其绘画，可以追溯到超过世纪和半。您的研究将使他们能够有效地索引收集的新方面，并以新的和引人入胜的方式向公众展示。