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.\S
3.3) on the Rare Word Similarity dataset.

本文提出一种方法学习语言的词汇，并使用图嵌入技术和跨语言向量空间映射方法结合本体源和语料库来提高现有词向量的覆盖范围以及加强其词汇知识。所提出的方法在 Rare Word Similarity dataset 上表现出 10% 的性能提升。