  To mine large digital libraries in humanistically meaningful ways, scholars
need to divide them by genre. This is a task that classification algorithms are
well suited to assist, but they need adjustment to address the specific
challenges of this domain. Digital libraries pose two problems of scale not
usually found in the article datasets used to test these algorithms. 1) Because
libraries span several centuries, the genres being identified may change
gradually across the time axis. 2) Because volumes are much longer than
articles, they tend to be internally heterogeneous, and the classification task
needs to begin with segmentation. We describe a multi-layered solution that
trains hidden Markov models to segment volumes, and uses ensembles of
overlapping classifiers to address historical change. We test this approach on
a collection of 469,200 volumes drawn from HathiTrust Digital Library. To
demonstrate the humanistic value of these methods, we extract 32,209 volumes of
fiction from the digital library, and trace the changing proportions of first-
and third-person narration in the corpus. We note that narrative points of view
seem to have strong associations with particular themes and genres.
