Memory-based parsing with parallel marker-passing

Abstract

Presents a parallel memory-based parser called PARALLEL, which is implemented on a marker-passing parallel AI computer called the Semantic Network Array Processor (SNAP). In the PARALLEL memory-based parser, the parallelism in natural language processing is utilized by a memory search model of parsing. Linguistic information is stored as phrasal patterns in a semantic network knowledge base that is distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking linguistic patterns that reflect a sentence interpretation. This is achieved via propagating markers over the distributed network. We have developed a system capable of processing newswire articles about terrorism with a large knowledge base of 12,000 semantic network nodes. This paper presents the structure of the system, the memory-based parsing method used and performance results obtained.<>