Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structural block driven - enhanced convolutional neural representation
  for relation extraction   In this paper, we propose a novel lightweight relation extraction approach of
structural block driven - convolutional neural learning. Specifically, we
detect the essential sequential tokens associated with entities through
dependency analysis, named as a structural block, and only encode the block on
a block-wise and an inter-block-wise representation, utilizing multi-scale
CNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence;
meanwhile 2) enhance the relevant block representation with both block-wise and
inter-block-wise semantically enriched representation. Our method has the
advantage of being independent of long sentence context since we only encode
the sequential tokens within a block boundary. Experiments on two datasets
i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our
method. In particular, we achieve the new state-of-the-art performance on the
KBP37 dataset; and comparable performance with the state-of-the-art on the
SemEval2010 dataset.

9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat