A Maximum Entropy Approach to Identifying Sentence Boundaries
<|reference_start|>A Maximum Entropy Approach to Identifying Sentence Boundaries: 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.<|reference_end|>
@article{reynar1997a,
    title={A Maximum Entropy Approach to Identifying Sentence Boundaries},
    author={Jeffrey C. Reynar (University of Pennsylvania), Adwait Ratnaparkhi
  (University of Pennsylvania)},
    journal={Proceedings of the 5th ANLP Conference, 1997},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9704002},
    primaryClass={cmp-lg cs.CL}
}