Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improve Document Embedding for Text Categorization Through Deep Siamese
  Neural Network   Due to the increasing amount of data on the internet, finding a
highly-informative, low-dimensional representation for text is one of the main
challenges for efficient natural language processing tasks including text
classification. This representation should capture the semantic information of
the text while retaining their relevance level for document classification.
This approach maps the documents with similar topics to a similar space in
vector space representation. To obtain representation for large text, we
propose the utilization of deep Siamese neural networks. To embed document
relevance in topics in the distributed representation, we use a Siamese neural
network to jointly learn document representations. Our Siamese network consists
of two sub-network of multi-layer perceptron. We examine our representation for
the text categorization task on BBC news dataset. The results show that the
proposed representations outperform the conventional and state-of-the-art
representations in the text classification task on this dataset.

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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph