Capturing the particular ways in which natural language interpretation proceeds is usually taken to involve rich abstract representations and fairly complex operations over such representations. From this perspective, two general goals of formal semantics are to identify patterns of interpretation that seem to involve such abstract (non-overt / latent) representations and operations and to design logical systems in which the ‘right’ range of representations and operators can be defined and in which these representations and operators interact in the ‘right’ way.

At the same time, providing solid empirical foundations for increasingly sophisticated formal semantics theories requires increasingly sophisticated methods of empirical investigation and statistical analysis of the resulting data. In addition, semantic theories should be complemented and further constrained by cognitive theories of how such structured, abstract and compositionally assembled representations and operations can be learned / induced from ‘raw’ observed data and the kinds of mechanisms that underlie their processing in actual natural language usage.

The overarching goal of the Language, Logic & Cognition (LaLoCo) Lab is to establish and solidify connections between:

  1. detailed, formally sophisticated linguistic theories — formal semantics theories in particular — and
  2. modern methods of data analysis and cognitive models of learning and processing abstract, highly structured representations of the kind deployed in formal semantics and generative linguistics more broadly.

For announcements, materials etc., please go to the LaLoCo Lab Log.