Abstract
We introduce negation to a symbolic-statistical modeling language PRISM and propose to eliminate negation by program transformation called negation technique which is applicable to probabilistic logic programs. We also introduce finite PCFGs (probabilistic context free grammars) as PCFGs with finite constraints as part of generative modeling of stochastic HPSGs (head-driven phrase structure grammars). They are a subclass of log-linear models and allow exact computation of normalizing constants. We apply the negation technique to a PDCG (probabilistic definite clause grammar) program written in PRISM that describes a finite PCFG with a height constraint. The resulting program computes a normalizing constant for the finite PCFG in time linear in the given height. We also report on an experiment of parameter learning for a real grammar (ATR grammar) with the height constraint. We have discovered that the height constraint does not necessarily lead to a significant decrease in parsing accuracy.
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Sato, T., Kameya, Y. (2005). Negation Elimination for Finite PCFGs. In: Etalle, S. (eds) Logic Based Program Synthesis and Transformation. LOPSTR 2004. Lecture Notes in Computer Science, vol 3573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11506676_8
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DOI: https://doi.org/10.1007/11506676_8
Publisher Name: Springer, Berlin, Heidelberg
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