Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mimicking Human Process: Text Representation via Latent Semantic
  Clustering for Classification   Considering that words with different characteristic in the text have
different importance for classification, grouping them together separately can
strengthen the semantic expression of each part. Thus we propose a new text
representation scheme by clustering words according to their latent semantics
and composing them together to get a set of cluster vectors, which are then
concatenated as the final text representation. Evaluation on five
classification benchmarks proves the effectiveness of our method. We further
conduct visualization analysis showing statistical clustering results and
verifying the validity of our motivation.

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The categories of this paper are: math
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
3
The categories of this paper are: physics
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The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat