TY - CONF
T1 - Methods of Large Grammar Representation in Massively Parallel Parsing Systems
AU - Winz, Stefan
AU - Geller, James
N1 - Funding Information:
This work was performed using the computational resources of the Northeast Parallel Architecture Center at Syracuse University, which is funded by and operates under contract to DARPA and the Airforce Systems Command, Rome Air Development Center, Griffiss Airforce Base, NY, under contract #F306002-88-C-0031.
Funding Information:
*Thisw orkw as performeuds ing the computationarle sourceso f the NortheastP arallel ArchitectureC entera t SyracuseU niversity,w hichi s funded by and operates under contract to DARPaAnd the Airforee SystemsC ommanRdo, meA ir DevelopmeCnte nter, Griffiss Airforce Base, NY,u nder contract #F306002-88-C-0031.
Publisher Copyright:
© 1993 AI Access Foundation. All rights reserved.
PY - 1993
Y1 - 1993
N2 - This paper describes techniques for massively parallel parsing where sequences of lexical categories are assigned to single processors and compared in parallel to a given input string. Because even small grammars result in full expansions that are much larger than the largest existing massively parallel computers, we need to develop techniques for "doubling up" sequences on processors so that they don't interfere during parallel matching. This paper describes three such techniques: (1) discrimination by length, (2) discrimination by open class/closed class words, and (3) combined discrimination by length and word class. We discuss possible reductions of the sequence space and implementation techniques on a CM-5 Connection Machine".
AB - This paper describes techniques for massively parallel parsing where sequences of lexical categories are assigned to single processors and compared in parallel to a given input string. Because even small grammars result in full expansions that are much larger than the largest existing massively parallel computers, we need to develop techniques for "doubling up" sequences on processors so that they don't interfere during parallel matching. This paper describes three such techniques: (1) discrimination by length, (2) discrimination by open class/closed class words, and (3) combined discrimination by length and word class. We discuss possible reductions of the sequence space and implementation techniques on a CM-5 Connection Machine".
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M3 - Paper
AN - SCOPUS:85168856498
SP - 225
EP - 233
T2 - 1993 AAAI Spring Symposium
Y2 - 23 March 1993 through 25 March 1993
ER -