Applying parallel processing to natural-language processing

Massively parallel computers offer not only improved speed but also a new perspective on computer vision, production systems, neural networks, and other AI applications. However, not much work has been done to apply parallel processing to natural-language processing, even though most sequential natural-language systems slow down as knowledge bases grow to realistic sizes and as linguistic features are added to handle special cases. To demonstrate the potential of parallel systems for natural-language processing, we selected an inherently parallel knowledge representation and reasoning method (marker-passing in a semantic network) and then developed a natural-language processor based on it. We implemented the memory-based parsing system/spl minus/called Parallel/spl minus/on a marker-passing parallel computer especially designed for natural-language processing.<
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