The first meeting of the Computational Humanities Initiative was held Friday November 18th in N400 of the Linguistics Department. Laure Thompson of the Manning College of Information and Computer Sciences presented “Computational Humanities and Human-Centered Machine Learning” to an audience that included UMass faculty and students from the departments of Classics, Philosophy, Economics, and Linguistics as well as from CICS, Nursing and SPHSS, and from Amherst and Mount Holyoke Colleges. The event was sponsored by the College of Humanities and Fine Arts and the Computational Social Science Institute. The Computational Humanities Initiative is being organized by faculty in CHFA and CICS interested in building new connections between our colleges in research and teaching. To find out about future events, join the Computational Humanities mailing list by emailing Joe Pater (email@example.com).
A recording of the talk is available to those with a UMass account, and an abstract and bio are below.
Thompson talk abstract: Machine learning (ML) is typically used to replicate some human activity. Given a set of inputs, a system is built to produce the same outputs as a human would; thus reducing human interaction through automation. In contrast to this standard use, the computational humanities typically use ML as a tool to enable human interaction in the form of human interpretation. This alternative use centers iterative, expert human use to study humanities collections in order to gain meaningful insights from and recognize the true complexities of cultural phenomena. In this talk, I will argue that these two uses are fundamentally different paradigms of user intention. I will illustrate the characteristics of these two paradigms using two case studies drawn from the computational humanities: a Dadaist “reading” of Dada and a largescale study of the themes in science fiction.
Bio: Laure Thompson is an assistant professor in the Manning College of Information & Computer Sciences at UMass Amherst, whose research bridges machine learning and natural language processing with humanistic scholarship. Centered on humanities applications, her research focuses on understanding what computational models actually learn and how we can intentionally change what they learn. Given this humanities focus, she works with a wide range of cultural heritage corpora: from texts of science fiction novels and medieval manuscripts to images of avant-garde journals and magical gems from the ancient Mediterranean. She completed her PhD in Computer Science at Cornell University in 2020.