Statistical Inference and Probabilistic Modelling for Constraint-Based NLP
| dc.creator | Riezler, Stefan | |
| dc.date | 1999-05-19 | |
| dc.date.accessioned | 2026-07-07T03:24:06Z | |
| dc.date.available | 2026-07-07T03:24:06Z | |
| dc.description | We present a probabilistic model for constraint-based grammars and a method for estimating the parameters of such models from incomplete, i.e., unparsed data. Whereas methods exist to estimate the parameters of probabilistic context-free grammars from incomplete data (Baum 1970), so far for probabilistic grammars involving context-dependencies only parameter estimation techniques from complete, i.e., fully parsed data have been presented (Abney 1997). However, complete-data estimation requires labor-intensive, error-prone, and grammar-specific hand-annotating of large language corpora. We present a log-linear probability model for constraint logic programming, and a general algorithm to estimate the parameters of such models from incomplete data by extending the estimation algorithm of Della-Pietra, Della-Pietra, and Lafferty (1997) to incomplete data settings. | |
| dc.description | 12 pages, uses knvns98.sty. Proceedings of the 4th Conference on Natural Language Processing (KONVENS-98) | |
| dc.identifier | https://arxiv.org/abs/cs/9905010 | |
| dc.identifier | http://arxiv.org/abs/cs/9905010 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/33200 | |
| dc.subject | Computation and Language | |
| dc.subject | Machine Learning | |
| dc.subject | I.2.6; I.2.7 | |
| dc.title | Statistical Inference and Probabilistic Modelling for Constraint-Based NLP | |
| dc.type | text |