  To disclose overlapped multiple relations from a sentence still keeps
challenging. Most current works in terms of neural models inconveniently
assuming that each sentence is explicitly mapped to a relation label, cannot
handle multiple relations properly as the overlapped features of the relations
are either ignored or very difficult to identify. To tackle with the new issue,
we propose a novel approach for multi-labeled relation extraction with capsule
network which acts considerably better than current convolutional or recurrent
net in identifying the highly overlapped relations within an individual
sentence. To better cluster the features and precisely extract the relations,
we further devise attention-based routing algorithm and sliding-margin loss
function, and embed them into our capsule network. The experimental results
show that the proposed approach can indeed extract the highly overlapped
features and achieve significant performance improvement for relation
extraction comparing to the state-of-the-art works.
