  We present a trainable model for identifying sentence boundaries in raw text.
Given a corpus annotated with sentence boundaries, our model learns to classify
each occurrence of ., ?, and ! as either a valid or invalid sentence boundary.
The training procedure requires no hand-crafted rules, lexica, part-of-speech
tags, or domain-specific information. The model can therefore be trained easily
on any genre of English, and should be trainable on any other Roman-alphabet
language. Performance is comparable to or better than the performance of
similar systems, but we emphasize the simplicity of retraining for new domains.
