Natural Language Processing for Art-Historical Information Extraction

In this studentship, you will work in the well-connected Natural Language Processing (NLP) research group at the University of Sheffield, which has a reputation for internationally leading research and is one of the largest of such groups in Europe. You will also collaborate closely with the prestigious National Gallery in London, which houses one of the greatest collections of paintings in the world.

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.

You will carry out 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. Your 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.

Your 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.

You will be expected to disseminate your research via top-tier national and international conferences (e.g., ACL, EMNLP, NAACL)and journals (e.g., TKDE, TKDD), and there will be a travel budget of £1000 a year to enable conference travel and research visits to the National Gallery. Domain experts will advise on the terminology and conventions used within the texts, and evaluate the accuracy and usefulness of the results.

Entry Requirements:

A very good undergraduate degree (at least a UK 2:1 honours degree, or international equivalent) or an MEng/MSc (or equivalent, or near completion) with first-class honours or distinction in Computer Science, or a closely related area. Knowledge of history of art and/or humanities computing is particularly welcome, though not essential.

Start date:

1 October 2022

Award and Eligibility:

The award covers Tuition Fees (UK student) and a minimum AHRC stipend of £16,062 per year towards living expenses, for up to 48 months. Outstanding EU/international students are also welcome to apply.

How to Apply:

To apply for a PhD studentship, applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Dr Diana Maynard as your proposed supervisor.

A link to the form and information on required supporting documents can be found here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying 

The form has comprehensive instructions for you to follow, and pop-up help is available.

Your research proposal should:

• be no longer than 4 A4 pages, including references
• outline your reasons for applying for this studentship
• explain how you would approach the research, including details of your skills and experience in [topic area]

For informal queries, please contact Dr Diana Maynard, e-mail: [Email Address Removed]