  This paper investigates techniques for knowledge injection into word
embeddings learned from large corpora of unannotated data. These
representations are trained with word cooccurrence statistics and do not
commonly exploit syntactic and semantic information from linguistic knowledge
bases, which potentially limits their transferability to domains with differing
language distributions or usages. We propose a novel approach for linguistic
knowledge injection through data augmentation to learn word embeddings that
enforce semantic relationships from the data, and systematically evaluate the
impact it has on the resulting representations. We show our knowledge
augmentation approach improves the intrinsic characteristics of the learned
embeddings while not significantly altering their results on a downstream text
classification task.
