Computational Linguistics in Museums: Applications for Cultural Datasets

As museums develop increasingly powerful tools for producing and publishing cultural data, many are beginning to face the challenge of optimizing a deluge of content for online visitors, while grappling with the requirement to organize and manage their growing datasets in local systems. And as they seek tools and methods for automating the management of information, both professionaland user-generated, they also hope to understand that information better. Museum professionals have begun to ask questions such as: how might user-generated comments be harvested and processed to determine the nature and meaning of the comment? Is it possible to use existing collection documentation as well as user-generated description to derive relations between similar objects? How can we train systems to automatically recognize (disambiguate) different meanings of the same word? Can automated language processing lead to more compelling browsing interfaces for online collections? Luckily, the field of computational linguistics brings a wealth of experience in dealing with complex data processing problems and a range of useful tools that can be applied to these problems to achieve practical, meaningful results. This paper presents work of the T3: Text, Tags, Trust project, an interdisciplinary collaboration of computational linguists, computer scientists, indexing and information retrieval experts, and museum professionals from the University of Maryland and Steve: The Museum Social Tagging Project. The authors define some key problems for managing largescale datasets, share tools and resources developed for the project, and describe ways that these resources can be deployed by museums without expertise in language processing. In addition, the paper examines some of the ways in which analysis of data collected by the Steve project builds on our understanding of the ways in which users see and describe our collections. The specific challenges of applying batch-processing tools and methods to large, unstructured datasets are addressed, best practices are shared for dealing with a number of sticky issues, and promising applications for future research and promising application areas are considered.