Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topic Model Based Multi-Label Classification from the Crowd   Multi-label classification is a common supervised machine learning problem
where each instance is associated with multiple classes. The key challenge in
this problem is learning the correlations between the classes. An additional
challenge arises when the labels of the training instances are provided by
noisy, heterogeneous crowdworkers with unknown qualities. We first assume
labels from a perfect source and propose a novel topic model where the present
as well as the absent classes generate the latent topics and hence the words.
We non-trivially extend our topic model to the scenario where the labels are
provided by noisy crowdworkers. Extensive experimentation on real world
datasets reveals the superior performance of the proposed model. The proposed
model learns the qualities of the annotators as well, even with minimal
training data.

12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
16
The categories of this paper are: stat
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an