Statistical Inference and Probabilistic Modelling for Constraint-Based NLP

Details Statistical Inference and Probabilistic Modelling for Constraint-Based NLP Statistical Inference and Probabilistic Modelling for Constraint-Based NLP

1999-05-19

arxiv.org/abs/cs/9905010v1

Computer Science

Computation and Language

12 pages, uses knvns98.sty. Proceedings of the 4th Conference on Natural Language Processing (KONVENS-98)

Scientific paper

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.

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