  Current breakthroughs in natural language processing have benefited
dramatically from neural language models, through which distributional
semantics can leverage neural data representations to facilitate downstream
applications. Since neural embeddings use context prediction on word
co-occurrences to yield dense vectors, they are inevitably prone to capture
more semantic association than semantic similarity. To improve vector space
models in deriving semantic similarity, we post-process neural word embeddings
through deep metric learning, through which we can inject lexical-semantic
relations, including syn/antonymy and hypo/hypernymy, into a distributional
space. We introduce hierarchy-fitting, a novel semantic specialization approach
to modelling semantic similarity nuances inherently stored in the IS-A
hierarchies. Hierarchy-fitting attains state-of-the-art results on the common-
and rare-word benchmark datasets for deriving semantic similarity from neural
word embeddings. It also incorporates an asymmetric distance function to
specialize hypernymy's directionality explicitly, through which it
significantly improves vanilla embeddings in multiple evaluation tasks of
detecting hypernymy and directionality without negative impacts on semantic
similarity judgement. The results demonstrate the efficacy of hierarchy-fitting
in specializing neural embeddings with semantic relations in late fusion,
potentially expanding its applicability to aggregating heterogeneous data and
various knowledge resources for learning multimodal semantic spaces.
