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

Explore the newspaper navigator.

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 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.

Ben's talk will be moderated by Jim Casey and Tianyi Wang

Register for this zoom webinar.

This event is part of the Machine Learning + Humanities Working Group Series and is co-sponsored by the Data Driven Social Science Initiative and the Center for Statistics and Machine Learning.