Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Approach for Expert Identification in Question Answering
  Communities   In this paper, we describe an effective convolutional neural network
framework for identifying the expert in question answering community. This
approach uses the convolutional neural network and combines user feature
representations with question feature representations to compute scores that
the user who gets the highest score is the expert on this question. Unlike
prior work, this method does not measure expert based on measure answer content
quality to identify the expert but only require question sentence and user
embedding feature to identify the expert. Remarkably, Our model can be applied
to different languages and different domains. The proposed framework is trained
on two datasets, The first dataset is Stack Overflow and the second one is
Zhihu. The Top-1 accuracy results of our experiments show that our framework
outperforms the best baseline framework for expert identification.

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The categories of this paper are: math
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The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph