Friday, April 4, 10 a.m., College Arts and Humanities Institute (1211 East Atwater Avenue)
Ayelet Ben-Yishai, Assistant Professor of English Language and Literature at the University of Haifa (Israel), will discuss her precirculated essay, "Walking the Boundaries: Realism as Communal Epistemology." Please come to meet Professor Ben-Yishai over coffee at the College Arts and Humanities Institute (CAHI). We will begin our discussion with Professor Ben-Yishai at 10 am.
Professor Ben-Yishai is the author of Common Precedents: The Presentness of the Past in Victorian Fiction and Law, which was published by Oxford University Press in 2013. With degrees in both Law (LL.B. Hebrew University 1996) and Literature (PhD, Comparative Literature, University of California, Berkeley 2005), she has published in both fields and
on their intersections.
A link to Professor Ben-Yishai's essay can be found in the Resources section
of the Victorian Studies Oncourse site.
Monday, April 7, 4 p.m., Woodburn 200
To the extent that humanists discuss computer science at all, we tend to imagine it instrumentally, as a source of useful "tools." But the conversation between computer scientists and humanists needn't be purely instrumental. Computer science is an epistemologically flexible discourse that seeks to model learning and belief; humanists may have more in common with it than we imagine.
To make these abstract reflections more concrete I'll describe a research project that tries to understand the history of poetic diction from 1700 to 1920. In this project, I've found computers useful for practical reasons: confronted with hundreds of thousands of digitized books, we need a way to identify the particular volumes and pages that contain "poetry," and a way to identify historically significant trends in hundreds of thousands of works. But beyond those practical problems, I've found the conceptual resources of computer science helpful as I grapple with the necessary ambiguities surrounding critical terms like "genre" and "diction." Methods discussed will include multi-label classification, semi-supervised learning, and probabilistic graphical models as well as versification and close reading.