Novel Machine Learning Methods for Computing Cultural Heritage: An Interdisciplinary Approach

Widespread efforts over the past two decades have drastically improved digital access to cultural heritage collections, transforming research for historians, sociologists, political scientists, and humanities researchers. Yet scholars and the public alike face a persistent challenge: how to navigate and analyze these collections, which frequently contain millions of items and often suffer from imperfect metadata.

Benjamin Lee (University of Washington) will share how his project, Newspaper Navigator, re-imagines how humanists, social scientists, and the public can navigate and analyze the visual content in millions of digitized historic newspaper pages. He will also introduce his ongoing work surrounding the development of open faceted search systems for petabyte-scale web archives and elaborate on how his research can extend to a wide range of digitized and born-digital collections.

The talk will be moderated by Jim Casey (Pennsylvania State University) and Tianyi Wang (Economics) and is part of the Machine Learning + Humanities Working Group series.

Register here.