  Distant supervision (DS) has been widely used to automatically construct
(noisy) labeled data for relation extraction (RE). Given two entities, distant
supervision exploits sentences that directly mention them for predicting their
semantic relation. We refer to this strategy as 1-hop DS, which unfortunately
may not work well for long-tail entities with few supporting sentences. In this
paper, we introduce a new strategy named 2-hop DS to enhance distantly
supervised RE, based on the observation that there exist a large number of
relational tables on the Web which contain entity pairs that share common
relations. We refer to such entity pairs as anchors for each other, and collect
all sentences that mention the anchor entity pairs of a given target entity
pair to help relation prediction. We develop a new neural RE method REDS2 in
the multi-instance learning paradigm, which adopts a hierarchical model
structure to fuse information respectively from 1-hop DS and 2-hop DS.
Extensive experimental results on a benchmark dataset show that REDS2 can
consistently outperform various baselines across different settings by a
substantial margin.
