1999/05/19 by Stefan Riezler, Riezler, Stefan
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.6 #I.2.7 #Machine Learning (cs.LG) #Natural Language Processing Techniques #Semantic Web and Ontologies #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.cs/9905010
12 pages, uses knvns98.sty. Proceedings of the 4th Conference on Natural Language Processing (KONVENS-98)
arxiv created 1999/05/19 · openalex publication_date 1999/05/19 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.