Towards a Model for Grounding Semantic Composition

Compositionality is widely accepted as a fundamental principle in linguistics and is also acknowledged as a key cognitive capacity. However, despite the prime importance of compositionality towards explaining the nature of meaning and concepts in cognition, and despite the need for computational models which are able to process the composition of grounded meaning there is little existing research. Thus, we aim to create computational models that concern the semantic composition of grounded meaning, that can be applied to embodied intelligent agents (such as cognitive robots), in order to make them capable of creating and processing grounded perceptual-semantic associations, and most importantly their compositions, taking into account syntactic, pragmatic as well as semantic considerations. Here we focus on an introduction to the problem, while then we review related work across multiple directions. Finally we propose a set of concrete desiderata that a computational theory of grounded semantic composition for embodied agents should satisfy, thus paving a clear avenue for the next steps towards the wider application of grounded semantics in intelligent embodied entities.