Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unveiling the semantic structure of text documents using paragraph-aware
  Topic Models   Classic Topic Models are built under the Bag Of Words assumption, in which
word position is ignored for simplicity. Besides, symmetric priors are
typically used in most applications. In order to easily learn topics with
different properties among the same corpus, we propose a new line of work in
which the paragraph structure is exploited. Our proposal is based on the
following assumption: in many text document corpora there are formal
constraints shared across all the collection, e.g. sections. When this
assumption is satisfied, some paragraphs may be related to general concepts
shared by all documents in the corpus, while others would contain the genuine
description of documents. Assuming each paragraph can be semantically more
general, specific, or hybrid, we look for ways to measure this, transferring
this distinction to topics and being able to learn what we call specific and
general topics. Experiments show that this is a proper methodology to highlight
certain paragraphs in structured documents at the same time we learn
interesting and more diverse topics.

7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin