  Distantly supervised relation extraction intrinsically suffers from noisy
labels due to the strong assumption of distant supervision. Most prior works
adopt a selective attention mechanism over sentences in a bag to denoise from
wrongly labeled data, which however could be incompetent when there is only one
sentence in a bag. In this paper, we propose a brand-new light-weight neural
framework to address the distantly supervised relation extraction problem and
alleviate the defects in previous selective attention framework. Specifically,
in the proposed framework, 1) we use an entity-aware word embedding method to
integrate both relative position information and head/tail entity embeddings,
aiming to highlight the essence of entities for this task; 2) we develop a
self-attention mechanism to capture the rich contextual dependencies as a
complement for local dependencies captured by piecewise CNN; and 3) instead of
using selective attention, we design a pooling-equipped gate, which is based on
rich contextual representations, as an aggregator to generate bag-level
representation for final relation classification. Compared to selective
attention, one major advantage of the proposed gating mechanism is that, it
performs stably and promisingly even if only one sentence appears in a bag and
thus keeps the consistency across all training examples. The experiments on NYT
dataset demonstrate that our approach achieves a new state-of-the-art
performance in terms of both AUC and top-n precision metrics.
