Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Neural Network Sentence Level Classification Method with Context
  Information   In the sentence classification task, context formed from sentences adjacent
to the sentence being classified can provide important information for
classification. This context is, however, often ignored. Where methods do make
use of context, only small amounts are considered, making it difficult to
scale. We present a new method for sentence classification, Context-LSTM-CNN,
that makes use of potentially large contexts. The method also utilizes
long-range dependencies within the sentence being classified, using an LSTM,
and short-span features, using a stacked CNN. Our experiments demonstrate that
this approach consistently improves over previous methods on two different
datasets.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas