  While word embeddings have been shown to implicitly encode various forms of
attributional knowledge, the extent to which they capture relational
information is far more limited. In previous work, this limitation has been
addressed by incorporating relational knowledge from external knowledge bases
when learning the word embedding. Such strategies may not be optimal, however,
as they are limited by the coverage of available resources and conflate
similarity with other forms of relatedness. As an alternative, in this paper we
propose to encode relational knowledge in a separate word embedding, which is
aimed to be complementary to a given standard word embedding. This relational
word embedding is still learned from co-occurrence statistics, and can thus be
used even when no external knowledge base is available. Our analysis shows that
relational word vectors do indeed capture information that is complementary to
what is encoded in standard word embeddings.
