Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Feature Representations for Keyphrase Extraction   In supervised approaches for keyphrase extraction, a candidate phrase is
encoded with a set of hand-crafted features and machine learning algorithms are
trained to discriminate keyphrases from non-keyphrases. Although the
manually-designed features have shown to work well in practice, feature
engineering is a difficult process that requires expert knowledge and normally
does not generalize well. In this paper, we present SurfKE, a feature learning
framework that exploits the text itself to automatically discover patterns that
keyphrases exhibit. Our model represents the document as a graph and
automatically learns feature representation of phrases. The proposed model
obtains remarkable improvements in performance over strong baselines.

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The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph