T
Ted Underwood
Researcher at University of Illinois at Urbana–Champaign
Publications - 53
Citations - 839
Ted Underwood is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Literary criticism & Literary science. The author has an hindex of 13, co-authored 45 publications receiving 671 citations. Previous affiliations of Ted Underwood include Colby College & Urbana University.
Papers
More filters
Journal ArticleDOI
Culture, Theory, Data: An Introduction
TL;DR: Culture, Theory, Data: An Introduction Ted Underwood, Laura McGrath, Richard Jean So (bio), and Chad Wellmon as mentioned in this paper discuss the role that computational methods and digital media play in contemporary research on culture.
Proceedings ArticleDOI
A Prototype Gutenberg-HathiTrust Sentence-level Parallel Corpus for OCR Error Analysis: Pilot Investigations
TL;DR: This exploratory study proposes a prototype sentence-level parallel corpus to support studying optical character recognition (OCR) quality in curated digitized library collections and conducts an analysis of OCR errors with a specific focus on their associations with the source text metadata.
Proceedings ArticleDOI
Improving Digital Libraries' Provision of Digital Humanities Datasets: A Case Study of HTRC Literature Dataset
TL;DR: This paper provides a use case utilizing an English literature dataset of 178,381 volumes curated by the HathiTrust Research Center (HTRC) for measuring the change of three literature genres.
Journal ArticleDOI
Descriptive Cataloging Issues for Non-Western Corpora: A Case Study of Late Imperial Chinese Books
TL;DR: In this article , a comparative case study of MARC records for Chinese (1,534), English (25,866), and German (7,872) books (the 1500s-1700s) reveals significant problems: data spread across fields; essential data lacunae; lack of normalization; and information transcribed from sources without translation nor explanation.
Proceedings Article
The Historical Significance of Textual Distances
TL;DR: The authors compare textual and social measures of the similarities between genres of English-language fiction, and compare existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that strive to anchor textual measurement in a social context.