Parallel natural language processing on a semantic network array processor

This paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, the Semantic Network Array Processor (SNAP). Our system uses a memory-based parsing approach in which parsing is viewed as a memory search process. Linguistic information is stored as phrasal patterns in a semantic network knowledge base distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking phrasal patterns that reflect a sentence interpretation. This is achieved by propagating markers over the distributed network. We have developed a system capable of processing newswire articles from a particular domain. The paper presents the structure of the system, the memory-based parsing method used, and the performance results obtained.