Title: Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach,
Abstract: Relation extraction is a fundamental task in information extraction. Most
existing methods have heavy reliance on annotations labeled by human experts,
which are costly and time-consuming. To overcome this drawback, we propose a
novel framework, REHession, to conduct relation extractor learning using
annotations from heterogeneous information source, e.g., knowledge base and
domain heuristics. These annotations, referred as heterogeneous supervision,
often conflict with each other, which brings a new challenge to the original
relation extraction task: how to infer the true label from noisy labels for a
given instance. Identifying context information as the backbone of both
relation extraction and true label discovery, we adopt embedding techniques to
learn the distributed representations of context, which bridges all components
with mutual enhancement in an iterative fashion. Extensive experimental results
demonstrate the superiority of REHession over the state-of-the-art.