  This thesis is concerned with type-logical grammars and their practical
applicability as tools of reasoning about sentence syntax and semantics. The
focal point is narrowed to Dutch, a language exhibiting a large degree of word
order variability. In order to overcome difficulties arising as a result of
that variability, the thesis explores and expands upon a type grammar based on
Multiplicative Intuitionistic Linear Logic, agnostic to word order but enriched
with decorations that aim to reduce its proof-theoretic complexity. An
algorithm for the conversion of dependency-annotated sentences into type
sequences is then implemented, populating the type logic with concrete,
data-driven lexical types. Two experiments are ran on the resulting grammar
instantiation. The first pertains to the learnability of the type-assignment
process by a neural architecture. A novel application of a self-attentive
sequence transduction model is proposed; contrary to established practices, it
constructs types inductively by internalizing the type-formation syntax, thus
exhibiting generalizability beyond a pre-specified type vocabulary. The second
revolves around a deductive parsing system that can resolve structural
ambiguities by consulting both word and type information; preliminary results
suggest both excellent computational efficiency and performance.
