Massively-Parallel Inferencing for Natural Language Understanding and Memory Retrieval in Structured Spreading-Activation Networks

One of the most difficult parts of the natural language understanding process is forming a semantic interpretation of the text. A reader must often make multiple inferences to understand the motives of actors and to causally connect actions that are unrelated on the basis of surface semantics alone. The inference process is complicated by the fact that text is often ambiguous both lexically and pragmatically, and that new context often forces a reinterpretation of the input’s meaning. This language understanding process itself does not exist in a vacuum -as people read text or hold conversations, they are often reminded of analogous stories or episodes. The types of memories that are triggered are influenced by context from the inferences and disambiguations of the understanding process. A full model of the language understanding and memory retrieval processes must take into account the interaction of the two and how they effect each other.