  Modelling semantic similarity plays a fundamental role in lexical semantic
applications. A natural way of calculating semantic similarity is to access
handcrafted semantic networks, but similarity prediction can also be
anticipated in a distributional vector space. Similarity calculation continues
to be a challenging task, even with the latest breakthroughs in deep neural
language models. We first examined popular methodologies in measuring taxonomic
similarity, including edge-counting that solely employs semantic relations in a
taxonomy, as well as the complex methods that estimate concept specificity. We
further extrapolated three weighting factors in modelling taxonomic similarity.
To study the distinct mechanisms between taxonomic and distributional
similarity measures, we ran head-to-head comparisons of each measure with human
similarity judgements from the perspectives of word frequency, polysemy degree
and similarity intensity. Our findings suggest that without fine-tuning the
uniform distance, taxonomic similarity measures can depend on the shortest path
length as a prime factor to predict semantic similarity; in contrast to
distributional semantics, edge-counting is free from sense distribution bias in
use and can measure word similarity both literally and metaphorically; the
synergy of retrofitting neural embeddings with concept relations in similarity
prediction may indicate a new trend to leverage knowledge bases on transfer
learning. It appears that a large gap still exists on computing semantic
similarity among different ranges of word frequency, polysemous degree and
similarity intensity.
