A Cognitive Framework for Core Language Understanding and its Computational Implementation

Abstract

The author argues that the cognitive processes underlying language understanding may not be logico-deductive or inductive, at least not for basic forms of understanding such as the ability to determine the topics of a text document. To demonstrate this point, they present a human cognition inspired framework for core language understanding and its computational implementation. The framework exploits word related knowledge stored in Long Term Memory (LTM) as well as Short Term Memory (STM) limited capacity, neuromorphic spreading activation and neural activation decay to derive the topics of text. The computational model implementing the framework shows the potential of the approach by establishing that the topics generated by the model are as good as those generated by humans.