Segmenting Messy Text: Detecting Boundaries in Text Derived from
  Historical Newspaper Images
Carol Anderson Phil Crone
Text segmentation, the task of dividing a document into sections, is often a
prerequisite for performing additional natural language processing tasks.
Existing text segmentation methods have typically been developed and tested
using clean, narrative-style text with segments containing distinct topics.
Here we consider a challenging text segmentation task: dividing newspaper
marriage announcement lists into units of one announcement each. In many cases
the information is not structured into sentences, and adjacent segments are not
topically distinct from each other. In addition, the text of the announcements,
which is derived from images of historical newspapers via optical character
recognition, contains many typographical errors. As a result, these
announcements are not amenable to segmentation with existing techniques. We
present a novel deep learning-based model for segmenting such text and show
that it significantly outperforms an existing state-of-the-art method on our
task.