Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Dimension Identification in User-Generated Text with LSTM
  Networks   In the process of online storytelling, individual users create and consume
highly diverse content that contains a great deal of implicit beliefs and not
plainly expressed narrative. It is hard to manually detect these implicit
beliefs, intentions and moral foundations of the writers. We study and
investigate two different tasks, each of which reflect the difficulty of
detecting an implicit user's knowledge, intent or belief that may be based on
writer's moral foundation: 1) political perspective detection in news articles
2) identification of informational vs. conversational questions in community
question answering (CQA) archives and. In both tasks we first describe new
interesting annotated datasets and make the datasets publicly available.
Second, we compare various classification algorithms, and show the differences
in their performance on both tasks. Third, in political perspective detection
task we utilize a narrative representation language of local press to identify
perspective differences between presumably neutral American and British press.

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The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph