  Manual annotation of the labeled data for relation extraction is
time-consuming and labor-intensive. Semi-supervised methods can offer helping
hands for this problem and have aroused great research interests. Existing work
focuses on mapping the unlabeled samples to the classes to augment the labeled
dataset. However, it is hard to find an overall good mapping function,
especially for the samples with complicated syntactic components in one
sentence.
  To tackle this limitation, we propose to build the connection between the
unlabeled data and the labeled ones rather than directly mapping the unlabeled
samples to the classes. Specifically, we first use three kinds of information
to construct reference graphs, including entity reference, verb reference, and
semantics reference. The goal is to semantically or lexically connect the
unlabeled sample(s) to the labeled one(s). Then, we develop a Multiple
Reference Graph (MRefG) model to exploit the reference information for better
recognizing high-quality unlabeled samples. The effectiveness of our method is
demonstrated by extensive comparison experiments with the state-of-the-art
baselines on two public datasets.
