Program transformations for optimization of parsing algorithms and other weighted logic programs

Dynamic programming algorithms in statistical natural language processing can be easily described as weighted logic programs. We give a notation and semantics for such programs. We then describe several source-to-source transformations that affect a program’s efficiency, primarily by rearranging computations for better reuse or by changing the search strategy. We present practical examples of using these transformations, mainly to optimize context-free parsing algorithms, and we formalize them for use with new weighted logic programs. Specifically, we define weighted versions of the folding and unfolding transformations, whose unweighted versions are used in the logic programming and deductive database communities. We then present a novel transformation called speculation—a powerful generalization of folding that is motivated by gap-passing in categorial grammar. Finally, we give a simpler and more powerful formulation of the magic templates transformation.1