  Distant supervision makes it possible to automatically label bags of
sentences for relation extraction by leveraging knowledge bases, but suffers
from the sparse and noisy bag issues. Additional information sources are
urgently needed to supplement the training data and overcome these issues. In
this paper, we introduce two widely-existing sources in knowledge bases, namely
entity descriptions, and multi-grained entity types to enrich the distantly
supervised data. We see information sources as multiple views and fusing them
to construct an intact space with sufficient information. An end-to-end
multi-view learning framework is proposed for relation extraction via Intact
Space Representation Learning (InSRL), and the representations of single views
are jointly learned simultaneously. Moreover, inner-view and cross-view
attention mechanisms are used to highlight important information on different
levels on an entity-pair basis. The experimental results on a popular benchmark
dataset demonstrate the necessity of additional information sources and the
effectiveness of our framework. We will release the implementation of our model
and dataset with multiple information sources after the anonymized review
phase.
