Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Probabilistic Models in Text Classification via Active
  Learning   When using text data, social scientists often classify documents in order to
use the resulting document labels as an outcome or predictor. Since it is
prohibitively costly to label a large number of documents manually, automated
text classification has become a standard tool. However, current approaches for
text classification do not take advantage of all the data at one's disposal. We
propose a fast new model for text classification that combines information from
both labeled and unlabeled data with an active learning component, where a
human iteratively labels documents that the algorithm is least certain about.
Using text data from Wikipedia discussion pages, BBC News articles, historical
US Supreme Court opinions, and human rights abuse allegations, we show that by
introducing information about the structure of unlabeled data and iteratively
labeling uncertain documents, our model improves performance relative to
classifiers that (a) only use information from labeled data and (b) randomly
decide which documents to label at the cost of manually labelling a small
number of documents.

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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex