Abstract
We report on a series of simulation experiments for a large-scale natural language processing system. The results indicate that an or-parallel, all solutions search provides substantial speed-up (20-30 fold) for this application. Longer sentences show relatively greater speed-up, with parse times that increase linearly with sentence length, given a sufficient number of processors. These results have been obtained using a simple, application-specific model of independent, non-communicating or-parallel processes in a shared memory environment. Simulations run with a range of overhead costs show significant benefits from parallel processing with per-process overheads ranging as high as the median process size; our estimates of overhead times are substantially smaller than median process size.
This is a revised and expanded version of Hirschman, Hopkins, and Smith, “Or-Parallel Speed- Up in Natural Language Processing: A Case Study,” published in Logic Programming: Proceedings of the Fifth International Conference and Symposium, MIT Press, 1988.
This research has been sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and the Rome Air Development Center (RADC) of the Air Force Systems Command under contract F30602-86-C-0093 and in part by DARPA under contract N00014-85-C-0012, administered by the Office of Naval Research. The new work reported here has been supported by Unisys Independent Research and Development funds.
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Hopkins, W.C., Hirschman, L., Smith, R.C. (1990). Or-Parallelism in Natural Language Parsing. In: Kumar, V., Gopalakrishnan, P.S., Kanal, L.N. (eds) Parallel Algorithms for Machine Intelligence and Vision. Symbolic Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3390-9_7
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