Learning Stylistic Meaning Across Modalities
Co-sponsored by the Berkeley Institute for Data Science, the School of Information, and the Department of Scandinavian.
Stylistic variation is meaningful across many cultural systems of communication, but attempts to understand the meaning embedded in style is complicated by the often blurry distinction between “style” and “content”. In this talk, I present a series of case studies in which we can take advantage of multimodal cultural artifacts including meme images, film, and websites to computationally model how different modalities of the same artifact can index different facets of meaning.
Space is limited. Submit the application form to request an invitation.
Speaker
Naitian Zhou
Naitian Zhou is a Ph.D. student at the UC Berkeley School of Information, where he is advised by David Bamman and supported by the NSF graduate research fellowship.
Naitian’s research centers on developing computational methods to understand meaning embedded in style. This draws on a variationist sociolinguistic / cultural anthropological perspective of culture, and spans the fields of NLP, computational social science, and cultural analytics. Methodologically, he is interested in multimodal approaches to language, vision and speech. He also cares a lot about the news, data journalism, data visualization and crossword puzzles.
Contact
If you have questions about this event, please contact Lilli Wessling Hart at lilli@berkeley.edu.
Accessibility
If you require an accommodation for effective communication (ASL interpreting, CART captioning, alternative media formats, etc.) or information about mobility access in order to fully participate in this event, please contact Lilli Wessling Hart at lilli@berkeley.edu with as much advance notice as possible and at least 7–10 days in advance of the event.