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Title:Mapping Genre at the Page Level in English-Language Volumes from HathiTrust, 1700-1899
Author(s):Underwood, Ted; Ballard, Shawn; Black, Michael L.; Capitanu, Boris
Subject(s):machine learning, genre, metadata, classification
Abstract:Using regularized logistic regression and hidden Markov models, we predict genre at the page level in a collection of 469,000 volumes from HathiTrust Digital Library. Accuracy is comparable to human crowdsourcing.
Issue Date:2014-07-10
Citation Info:Underwood, T., Ballard, S., Black, M.L., and Capitanu, B. "Mapping Genre at the Page Level in English-Language Volumes from HathiTrust, 1700-1899." Poster at DH2014. Lausanne, July 2014.
Genre:Conference Poster
Type:Image
Language:English
URI:http://hdl.handle.net/2142/50291
Date Available in IDEALS:2014-09-01


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