This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways (rather than exploring the core algorithms underlying them; see Info 159/259 for that).
Ted Underwood teaches in the School of Information Sciences and the English Department at the University of Illinois, Urbana-Champaign. He was trained as a Romanticist and now applies machine learning to large digital collections. His most recent book, Distant Horizons: Digital Evidence and Literary Change (Univ of Chicago, Spring 2019) addresses new perspectives opened up by large digital libraries.
Seminar reading:
Laura McGrath is the Associate Director of the Stanford University Literary Lab and a postdoctoral fellow in English. Her primary interests lie in computational approaches to post45 American fiction. She is at work on a manuscript, a literary history of the agent, entitled Middlemen: Making Literature in the Age of Multimedia Conglomerates. She is also working on a second, trade book called Comps: The Big Data Behind the Book Business.
Seminar readings:
This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism).
The rise of large-scale digitized book collections—such as those provided by Google Books, the HathiTrust and the Internet Archive—is enabling a fundamentally new kind of text analysis that exploits the scale of collections to ask questions not possible with smaller corpora.
The rise of large-scale digitized book collections—such as those provided by Google Books, the HathiTrust and the
Internet Archive—is enabling a fundamentally new kind of text analysis that exploits the scale of collections to ask
questions not possible with smaller corpora. Many of these research questions are driven by historically deep textual
collections—corpora that span several decades or centuries in their publication. Moretti (2007) analyzes the changing