Mapping mutable genres in structurally complex volumes

@article{Underwood2013MappingMG,
  title={Mapping mutable genres in structurally complex volumes},
  author={T. Underwood and M. Black and L. Auvil and Boris Capitanu},
  journal={2013 IEEE International Conference on Big Data},
  year={2013},
  pages={95-103}
}
  • T. Underwood, M. Black, +1 author Boris Capitanu
  • Published 2013
  • Computer Science
  • 2013 IEEE International Conference on Big Data
  • To mine large digital libraries in humanistically meaningful ways, we need to divide them by genre. [...] Key Method We describe a multilayered solution that trains hidden Markov models to segment volumes, and uses ensembles of overlapping classifers to address historical change. We demonstrate this on a collection of 469,200 volumes drawn from HathiTrust Digital Library.Expand Abstract
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