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Computational Linguistics in Museums: Applications for Cultural Datasets
Judith Klavans, University of Maryland; Robert Stein, Indianapolis Museum of Art; Susan Chun, Independent Consultant and Researcher; Raul David Guerra, University of Maryland, USA
Abstract
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 large-scale 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 for dealing with a number of sticky issues are shared, and promising applications for future research and promising application areas are considered.
Keywords: computational linguistics, social tagging, steve.museum, collection access, text processing, metadata
Computational Linguistics in Museums: Applications for Cultural Datasets
As museums continue to develop more sophisticated techniques for managing and analyzing cultural data, many are beginning to encounter challenges when trying to deal with the nuances of language and automated processing tools. How might user-generated comments be harvested and processed to determine the nature of the comment? Is it possible to use existing collection documentation t