This paper describes a semantically based computational theory of natural language comprehension. The theory argues for a semantically rich lexicon whose entries can be described as monosemic, generative and image-like. The comprehension process uses the basic definition of a word to decide how new information is to be combined with what has been interpreted so far. Next, and more importantly, the background information is used to generate the meaning of the combined words. Other semantically based approaches are also reviewed, one each from the disciplines of AI, Cognitive Science, and Linguistics.

The cognitive maps that humans compute as representations of the spatial environment they have visited are rarely even close approximations to what was actually experienced. When we experience the environment we seem to see it all so perfectly, yet rarely are we able to reproduce from memory an exact description of the places visited. Yet these vague, muddled descriptions of the places visited are adequate for many spatial reasoning tasks. But how is such an impoverished representation computed from what is initially delivered by one's senses? And what effect does this representation have on the construction of the cognitive map? W e present one method for computing a vague description of each local space visited. It is derived from the initially accurate description needed for the actions the viewer might perform within the local space. We show the effect of this representation on the structure of the cognitive map.