Computer reading can feel like a Faustian bargain. Sure, we can learn about linguistic patterns in literary texts, but it comes at the expense of their richness. At bottom, the computer simply doesn't know what or how words mean. Instead, it merely recognizes strings of characters and tallies them. Statistical models then try to identify relationships among the tallies. How could this begin to capture anything like irony or affect or subjectivity that we take as our entry point to interpretive study?

This fall, David Bamman joined the School of Information faculty as an Assistant Professor. His research in natural language processing and machine learning has direct applications for digital humanities scholarship. Bamman himself has a background in the humanities, including undergraduate studies in classics and English literature at the University of Wisconsin-Madison and an M.A. in Applied Linguistics from Boston University.

This summer, Kyle Booten, Ph.D. candidate in Education with a designated emphasis in New Media, explored the fundamentals of Python programming through digital poetry with his undergraduate students. Meeting for six short weeks at the Berkeley Center for New Media, “Poetry and Technology: A Digital Verse Lab” students worked together in groups to produce works of digital poetry.

Marti Hearst, Professor of Information and Electrical Engineering and Computer Science, recently presented a keynote titled, “Can Natural Language Processing Become Natural Language Coaching?” at the annual meeting of the Association of Computational Linguistics. Hosted in Beijing, this year’s conference attracted 950 attendees.