Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Gated Recurrent Units for Medical Relation Classification   Convolutional neural network (CNN) and recurrent neural network (RNN) models
have become the mainstream methods for relation classification. We propose a
unified architecture, which exploits the advantages of CNN and RNN
simultaneously, to identify medical relations in clinical records, with only
word embedding features. Our model learns phrase-level features through a CNN
layer, and these feature representations are directly fed into a bidirectional
gated recurrent unit (GRU) layer to capture long-term feature dependencies. We
evaluate our model on two clinical datasets, and experiments demonstrate that
our model performs significantly better than previous single-model methods on
both datasets.

18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th