  Word embeddings have been found to capture a surprisingly rich amount of
syntactic and semantic knowledge. However, it is not yet sufficiently
well-understood how the relational knowledge that is implicitly encoded in word
embeddings can be extracted in a reliable way. In this paper, we propose two
probabilistic models to address this issue. The first model is based on the
common relations-as-translations view, but is cast in a probabilistic setting.
Our second model is based on the much weaker assumption that there is a linear
relationship between the vector representations of related words. Compared to
existing approaches, our models lead to more accurate predictions, and they are
more explicit about what can and cannot be extracted from the word embedding.
