Inter-sentence Relation Extraction with Document-levelGraph Convolutional Neural Network
Abstract
Inter-sentence relation extraction deals with
a number of complex semantic relationships
in documents, which require local, non-local,
syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence
relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The
graph is constructed using various inter- and
intra-sentence dependencies to capture local
and non-local dependency information. In order to predict the relation of an entity pair, we
utilise multi-instance learning with bi-affine
pairwise scoring. Experimental results show
that our model achieves comparable performance to the state-of-the-art neural models on
two biochemistry datasets. Our analysis shows
that all the types in the graph are effective for
inter-sentence relation extraction