# Indirectly Supervised English Sentence Break Prediction Using Paragraph Break Probability Estimates

arXiv: https://arxiv.org/abs/2109.12023 · HTML: https://mlsanity.com/paper/2109.12023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation](https://mlsanity.com/paper/2305.18893/) (2023)

## Abstract (arXiv)

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.