Statistical Inference and Probabilistic Modelling for Constraint-Based NLP
<|reference_start|>Statistical Inference and Probabilistic Modelling for Constraint-Based NLP: 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.<|reference_end|>
@article{riezler1999statistical,
    title={Statistical Inference and Probabilistic Modelling for Constraint-Based
  NLP},
    author={Stefan Riezler (IMS, University of Stuttgart)},
    journal={arXiv preprint arXiv:cs/9905010},
    year={1999},
    archivePrefix={arXiv},
    eprint={cs/9905010},
    primaryClass={cs.CL cs.LG}
}