  This report explores the use of paragraph break probability estimates to help
predict the location of sentence breaks in English natural language text. We
show that a sentence break predictor based almost solely on paragraph break
probability estimates can achieve high accuracy on this task. This sentence
break predictor is trained almost entirely on a large amount of naturally
occurring text without sentence break annotations, with only a small amount of
annotated data needed to tune two hyperparameters. We also show that even
better results can be achieved across in-domain and out-of-domain test data, if
paragraph break probability signals are combined with a support vector machine
classifier trained on a somewhat larger amount of sentence-break-annotated
data. Numerous related issues are addressed along the way.
