  The exploitation of syntactic graphs (SyGs) as a word's context has been
shown to be beneficial for distributional semantic models (DSMs), both at the
level of individual word representations and in deriving phrasal
representations via composition. However, notwithstanding the potential
performance benefit, the syntactically-aware DSMs proposed to date have huge
numbers of parameters (compared to conventional DSMs) and suffer from data
sparsity. Furthermore, the encoding of the SyG links (i.e., the syntactic
relations) has been largely limited to linear maps. The knowledge graphs'
literature, on the other hand, has proposed light-weight models employing
different geometric transformations (GTs) to encode edges in a knowledge graph
(KG). Our work explores the possibility of adopting this family of models to
encode SyGs. Furthermore, we investigate which GT better encodes syntactic
relations, so that these representations can be used to enhance phrase-level
composition via syntactic contextualisation.
