Learn More
The SNAP information extraction system has been developed as a part of a three-year SNAP projec t sponsored by the National Science Foundation. The main goal of the SNAP project is to build a massively parallel computer capable of fast and accurate natural language processing [5] . Throughout the project, a parallel computer was built in the Parallel(More)
This paper describes a natural language processing system developed for the Semantic Network Array Processor (SNAP). The goal of our work is to develop a scalable and high-performance natural language processing system which utilizes the high degree of parallelism provided by the SNAP machine. We have implemented an experimental machine translation system(More)
During the final week of testing, our system was run on test sets TST3 and TST4 . Test set TST3 contains 100 articles from the same time period as the training corpus (DEV), and test sets TST1 and TST2 . The summary of score results for TST3 is shown in Table 1 . Test set TST4 contains 100 articles from a differen t time period then those of TST3. The(More)
The main goal of the SNAP project is to build a massively parallel computer capable of fast and accurat e natural language processing [3] . Under NSF funding, a parallel computer was built in the Parallel Knowledg e Processing Laboratory at USC and software was developed to operate the machine [2] . The approach i n designing SNAP was to find a knowledge(More)
This paper describes a memory-based machine translation system developed for the Semantic Network Array Processor (SNAP). The goal of our work is to develop a scalable and high-performance memory-based machine translation system which utilizes the high degree of parallelism provided by the SNAP machine. We have implemented an experimental machine(More)
  • 1