  Universal schema (USchema) assumes that two sentence patterns that share the
same entity pairs are similar to each other. This assumption is widely adopted
for solving various types of relation extraction (RE) tasks. Nevertheless, each
sentence pattern could contain multiple facets, and not every facet is similar
to all the facets of another sentence pattern co-occurring with the same entity
pair. To address the violation of the USchema assumption, we propose
multi-facet universal schema that uses a neural model to represent each
sentence pattern as multiple facet embeddings and encourage one of these facet
embeddings to be close to that of another sentence pattern if they co-occur
with the same entity pair. In our experiments, we demonstrate that multi-facet
embeddings significantly outperform their single-facet embedding counterpart,
compositional universal schema (CUSchema) (Verga et al., 2016), in distantly
supervised relation extraction tasks. Moreover, we can also use multiple
embeddings to detect the entailment relation between two sentence patterns when
no manual label is available.
