  Chemical-disease relation (CDR) extraction is significantly important to
various areas of biomedical research and health care. Nowadays, many
large-scale biomedical knowledge bases (KBs) containing triples about entity
pairs and their relations have been built. KBs are important resources for
biomedical relation extraction. However, previous research pays little
attention to prior knowledge. In addition, the dependency tree contains
important syntactic and semantic information, which helps to improve relation
extraction. So how to effectively use it is also worth studying. In this paper,
we propose a novel convolutional attention network (CAN) for CDR extraction.
Firstly, we extract the shortest dependency path (SDP) between chemical and
disease pairs in a sentence, which includes a sequence of words, dependency
directions, and dependency relation tags. Then the convolution operations are
performed on the SDP to produce deep semantic dependency features. After that,
an attention mechanism is employed to learn the importance/weight of each
semantic dependency vector related to knowledge representations learned from
KBs. Finally, in order to combine dependency information and prior knowledge,
the concatenation of weighted semantic dependency representations and knowledge
representations is fed to the softmax layer for classification. Experiments on
the BioCreative V CDR dataset show that our method achieves comparable
performance with the state-of-the-art systems, and both dependency information
and prior knowledge play important roles in CDR extraction task.
