Title: Learning Rare Word Representations using Semantic Bridging,
Abstract: We propose a methodology that adapts graph embedding techniques (DeepWalk
(Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as
cross-lingual vector space mapping approaches (Least Squares and Canonical
Correlation Analysis) in order to merge the corpus and ontological sources of
lexical knowledge. We also perform comparative analysis of the used algorithms
in order to identify the best combination for the proposed system. We then
apply this to the task of enhancing the coverage of an existing word
embedding's vocabulary with rare and unseen words. We show that our technique
can provide considerable extra coverage (over 99%), leading to consistent
performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.§
3.3) on the Rare Word Similarity dataset.