2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/33200We 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.12 pages, uses knvns98.sty. Proceedings of the 4th Conference on Natural Language Processing (KONVENS-98)Computation and LanguageMachine LearningI.2.6; I.2.7text