  We study the problem of textual relation embedding with distant supervision.
To combat the wrong labeling problem of distant supervision, we propose to
embed textual relations with global statistics of relations, i.e., the
co-occurrence statistics of textual and knowledge base relations collected from
the entire corpus. This approach turns out to be more robust to the training
noise introduced by distant supervision. On a popular relation extraction
dataset, we show that the learned textual relation embedding can be used to
augment existing relation extraction models and significantly improve their
performance. Most remarkably, for the top 1,000 relational facts discovered by
the best existing model, the precision can be improved from 83.9% to 89.3%.
