  Extracting relations from text corpora is an important task in text mining.
It becomes particularly challenging when focusing on weakly-supervised relation
extraction, that is, utilizing a few relation instances (i.e., a pair of
entities and their relation) as seeds to extract more instances from corpora.
Existing distributional approaches leverage the corpus-level co-occurrence
statistics of entities to predict their relations, and require large number of
labeled instances to learn effective relation classifiers. Alternatively,
pattern-based approaches perform bootstrapping or apply neural networks to
model the local contexts, but still rely on large number of labeled instances
to build reliable models. In this paper, we study integrating the
distributional and pattern-based methods in a weakly-supervised setting, such
that the two types of methods can provide complementary supervision for each
other to build an effective, unified model. We propose a novel co-training
framework with a distributional module and a pattern module. During training,
the distributional module helps the pattern module discriminate between the
informative patterns and other patterns, and the pattern module generates some
highly-confident instances to improve the distributional module. The whole
framework can be effectively optimized by iterating between improving the
pattern module and updating the distributional module. We conduct experiments
on two tasks: knowledge base completion with text corpora and corpus-level
relation extraction. Experimental results prove the effectiveness of our
framework in the weakly-supervised setting.
