Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Neural Networks for Sequential Sentence Classification in
  Medical Scientific Abstracts   Prevalent models based on artificial neural network (ANN) for sentence
classification often classify sentences in isolation without considering the
context in which sentences appear. This hampers the traditional sentence
classification approaches to the problem of sequential sentence classification,
where structured prediction is needed for better overall classification
performance. In this work, we present a hierarchical sequential labeling
network to make use of the contextual information within surrounding sentences
to help classify the current sentence. Our model outperforms the
state-of-the-art results by 2%-3% on two benchmarking datasets for sequential
sentence classification in medical scientific abstracts.

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The categories of this paper are: math
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn