Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning beyond Predefined Label Space via Bayesian Nonparametric Topic
  Modelling   In real world machine learning applications, testing data may contain some
meaningful new categories that have not been seen in labeled training data. To
simultaneously recognize new data categories and assign most appropriate
category labels to the data actually from known categories, existing models
assume the number of unknown new categories is pre-specified, though it is
difficult to determine in advance. In this paper, we propose a Bayesian
nonparametric topic model to automatically infer this number, based on the
hierarchical Dirichlet process and the notion of latent Dirichlet allocation.
Exact inference in our model is intractable, so we provide an efficient
collapsed Gibbs sampling algorithm for approximate posterior inference.
Extensive experiments on various text data sets show that: (a) compared with
parametric approaches that use pre-specified true number of new categories, the
proposed nonparametric approach can yield comparable performance; and (b) when
the exact number of new categories is unavailable, i.e. the parametric
approaches only have a rough idea about the new categories, our approach has
evident performance advantages.

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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con