A dynamic associative semantic model for natural language processing based on a spreading activation network

Abstract

This paper presents a semantic model based on well-known psycholinguistic theories of human memory. It is centered on a spreading activation network, but it departs from classical models by representing associations between structured units instead of atomic nodes. Network units have an activity level that evolves according to their expected contextual relevance. Spreading activation explains the predictive top-down effect of knowledge. It supports general heuristics which may be used as the first step of more elaborated methods. This model is suited to deal with the interaction between semantic and episodic memories, as well as many other practical issues regarding natural language processing, including the retroactive effect of semantics over perception and the operation in open-worlds.