  Data-Oriented Parsing (dop) ranks among the best parsing schemes, pairing
state-of-the art parsing accuracy to the psycholinguistic insight that larger
chunks of syntactic structures are relevant grammatical and probabilistic
units. Parsing with the dop-model, however, seems to involve a lot of CPU
cycles and a considerable amount of double work, brought on by the concept of
multiple derivations, which is necessary for probabilistic processing, but
which is not convincingly related to a proper linguistic backbone. It is
however possible to re-interpret the dop-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 parse. By emphasizing this memory-based
aspect of the dop-model, it is possible to do away with multiple derivations,
opening up possibilities for efficient Viterbi-style optimizations, while still
retaining acceptable parsing accuracy through enhanced context-sensitivity.
