Abstract
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However,
how to effectively make use of relevant information while ignoring irrelevant information
from the dependency trees remains a challenging research question. Existing approaches
employing rule based hard-pruning strategies
for selecting relevant partial dependency structures may not always yield optimal results. In
this work, we propose Attention Guided Graph
Convolutional Networks (AGGCNs), a novel
model which directly takes full dependency
trees as inputs. Our model can be understood
as a soft-pruning approach that automatically
learns how to selectively attend to the relevant
sub-structures useful for the relation extraction task. Extensive results on various tasks
including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the
full dependency trees, giving significantly better results than previous approaches.