Aspects of Pattern-matching in Data-Oriented Parsing

Data-Oriented Parsing (DOP) ranks among the best parsing schemes, pairing state-of-the m't I)arsing accuracy to the psycboliliguistic insight that larger clmnks of syntactic sl;ructures are relevant grammatical and probabilistic units. Parsing with the pOp-model, howev(;r, seems to involve a lot of CPU cycles and a considerable alnount of double work, brought on by the concept of multiple deriwtions, which is necessary for probabilistic processing, but which is not convincingly related to a proper linguistic backl)one. It is however 1)ossible to re- interpret the poP-model as a pattern-matching model, which tries to maximize the size of the substructures that construct the parse, rather than the probability of the pars(;. By emphasizing this memory-based aspect of the pop-model, it is possible to do away with multiple derivations, opening Ul) possibilities fbr efficient Viterbistyle optimizations, while still fetal,ting acceptable parslug accuracy through enhanced context-sensitivity