  Human beings possess the most sophisticated computational machinery in the
known universe. We can understand language of rich descriptive power, and
communicate in the same environment with astonishing clarity. Two of the many
contributors to the interest in natural language - the properties of
Compositionality and Structure Dependence, are well documented, and offer a
vast space to ask interesting modelling questions. The first step to begin
answering these questions is to ground verbal theory in formal terms. Drawing
on linguistics and set theory, a formalisation of these ideas is presented in
the first half of this thesis. We see how cognitive systems that process
language need to have certain functional constraints, viz. time based,
incremental operations that rely on a structurally defined domain. The
observations that result from analysing this formal setup are examined as part
of a modelling exercise. Using the advances of word embedding techniques, a
model of relational learning is simulated with a custom dataset to demonstrate
how a time based role-filler binding mechanism satisfies some of the
constraints described in the first section. The model's ability to map
structure, along with its symbolic-connectionist architecture makes for a
cognitively plausible implementation. The formalisation and simulation are
together an attempt to recognise the constraints imposed by linguistic theory,
and explore the opportunities presented by a cognitive model of relation
learning to realise these constraints.
