  Existing methods in relation extraction have leveraged the lexical features
in the word sequence and the syntactic features in the parse tree. Though
effective, the lexical features extracted from the successive word sequence may
introduce some noise that has little or no meaningful content. Meanwhile, the
syntactic features are usually encoded via graph convolutional networks which
have restricted receptive field. To address the above limitations, we propose a
multi-scale feature and metric learning framework for relation extraction.
Specifically, we first develop a multi-scale convolutional neural network to
aggregate the non-successive mainstays in the lexical sequence. We also design
a multi-scale graph convolutional network which can increase the receptive
field towards specific syntactic roles. Moreover, we present a multi-scale
metric learning paradigm to exploit both the feature-level relation between
lexical and syntactic features and the sample-level relation between instances
with the same or different classes. We conduct extensive experiments on three
real world datasets for various types of relation extraction tasks. The results
demonstrate that our model significantly outperforms the state-of-the-art
approaches.
