  Building machines that can understand text like humans is an AI-complete
problem. A great deal of research has already gone into this, with astounding
results, allowing everyday people to discuss with their telephones, or have
their reading materials analysed and classified by computers. A prerequisite
for processing text semantics, common to the above examples, is having some
computational representation of text as an abstract object. Operations on this
representation practically correspond to making semantic inferences, and by
extension simulating understanding text. The complexity and granularity of
semantic processing that can be realised is constrained by the mathematical and
computational robustness, expressiveness, and rigour of the tools used.
  This dissertation contributes a series of such tools, diverse in their
mathematical formulation, but common in their application to model semantic
inferences when machines process text. These tools are principally expressed in
nine distinct models that capture aspects of semantic dependence in highly
interpretable and non-complex ways. This dissertation further reflects on
present and future problems with the current research paradigm in this area,
and makes recommendations on how to overcome them.
  The amalgamation of the body of work presented in this dissertation advances
the complexity and granularity of semantic inferences that can be made
automatically by machines.
