Grounding Lexical Meaning in Core Cognition
Noah D. Goodman · 2013
Author’s note: This document is a slightly updated and reformatted extract from a grant proposal to the ONR. As a proposal, it aims describe useful directions while reviewing existing and pilot work; it has no pretensions to being a systematic, rigorous, or entirely coherent scholarly work. On the other hand, I’ve found that it provides a useful overview of a few ideas on the architecture of natural language that haven’t yet appeared elsewhere. I provide it for those interested, but with all due caveats. Words are potentially one of the clearest windows on human knowledge and conceptual structure. But what do words mean? In this project we aim to construct and explore a formal model of lexical semantics grounded, via pragmatic inference, in core conceptual structures. Flexible human cognition is derived in large part from our ability to imagine possible worlds. A rich set of concepts, intuitive theories, and other mental representations support imagining and reasoning about possible worlds—together we call these core cognition. Here we posit that the collection of core concepts also forms the set of primitive elements available for lexical semantics: word meanings are built from pieces of core cognition. We propose to study lexical semantics in the setting of an architecture for language understanding that integrates literal meaning with pragmatic inference. This architecture supports underspecified and uncertain lexical meaning, leading to subtle interactions between meaning, conceptual structure, and context. We will explore several cases of lexical semantics where these interactions are particularly important: indexicals, scalar adjectives, generics, and modals. We formalize both core cognition and the natural language architecture using the Church probabilistic programming language. In this project we aim to contribute to our understanding of the connection between words and mental representations; from this we expect to gain critical insights into many aspects of psychology, to construct vastly more useful thinking machines, and to interface natural and artificial intelligences more efficiently.