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This report explores the use of paragraph break probability estimates to helppredict the location of sentence breaks in English natural language text. Weshow that a sentence break predictor based almost solely on paragraph breakprobability estimates can achieve high accuracy on this task. This sentencebreak predictor is trained almost entirely on a large amount of naturallyoccurring text without sentence break annotations, with only a small amount ofannotated data needed to tune two hyperparameters. We also show that evenbetter results can be achieved across in-domain and out-of-domain test data, ifparagraph break probability signals are combined with a support vector machineclassifier trained on a somewhat larger amount of sentence-break-annotateddata. Numerous related issues are addressed along the way.

Indirectly Supervised English Sentence Break Prediction Using Paragraph Break Probability Estimates