Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Citation Data-set for Machine Learning Citation Styles and Entity
  Extraction from Citation Strings   Citation parsing is fundamental for search engines within academia and the
protection of intellectual property. Meticulous extraction is further needed
when evaluating the similarity of documents and calculating their citation
impact. Citation parsing involves the identification and dissection of citation
strings into their bibliography components such as "Author", "Volume","Title",
etc. This meta-data can be difficult to acquire accurately due to the thousands
of different styles and noise that can be applied to a bibliography to create
the citation string. Many approaches exist already to accomplish accurate
parsing of citation strings. This dissertation describes the creation of a
large data-set which can be used to aid in the training of these approaches
which have limited data.It also describes the investigation into if the
downfall of these approaches to citation parsing and in particular the machine
learning based approaches is because of the lack of size associated to the data
used to train them.

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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: math
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex