Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simplifying Impact Prediction for Scientific Articles   Estimating the expected impact of an article is valuable for various
applications (e.g., article/cooperator recommendation). Most existing
approaches attempt to predict the exact number of citations each article will
receive in the near future, however this is a difficult regression analysis
problem. Moreover, most approaches rely on the existence of rich metadata for
each article, a requirement that cannot be adequately fulfilled for a large
number of them. In this work, we take advantage of the fact that solving a
simpler machine learning problem, that of classifying articles based on their
expected impact, is adequate for many real world applications and we propose a
simplified model that can be trained using minimal article metadata. Finally,
we examine various configurations of this model and evaluate their
effectiveness in solving the aforementioned classification problem.

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The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
4
The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph