Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A General-Purpose Tagger with Convolutional Neural Networks   We present a general-purpose tagger based on convolutional neural networks
(CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific
tuning of hyper-parameters, it achieves state-of-the-art results in
part-of-speech tagging, morphological tagging and supertagging. The CNN tagger
is also robust against the out-of-vocabulary problem, it performs well on
artificially unnormalized texts.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio