A Maximum Entropy Approach to Identifying Sentence Boundaries
| dc.creator | Reynar, Jeffrey C. | |
| dc.creator | Ratnaparkhi, Adwait | |
| dc.date | 1997-04-09 | |
| dc.date.accessioned | 2026-07-07T09:10:45Z | |
| dc.date.available | 2026-07-07T09:10:45Z | |
| dc.description | 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. | |
| dc.description | 4 pages, uses aclap.sty and covingtn.sty | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9704002 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9704002 | |
| dc.identifier | Proceedings of the 5th ANLP Conference, 1997 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151443 | |
| dc.subject | Computation and Language | |
| dc.title | A Maximum Entropy Approach to Identifying Sentence Boundaries | |
| dc.type | text |