Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 2-gram-based Phonetic Feature Generation for Convolutional Neural
  Network in Assessment of Trademark Similarity   A trademark is a mark used to identify various commodities. If same or
similar trademark is registered for the same or similar commodity, the
purchaser of the goods may be confused. Therefore, in the process of trademark
registration examination, the examiner judges whether the trademark is the same
or similar to the other applied or registered trademarks. The confusion in
trademarks is based on the visual, phonetic or conceptual similarity of the
marks. In this paper, we focus specifically on the phonetic similarity between
trademarks. We propose a method to generate 2D phonetic feature for
convolutional neural network in assessment of trademark similarity. This
proposed algorithm is tested with 12,553 trademark phonetic similar pairs and
34,020 trademark phonetic non-similar pairs from 2010 to 2016. As a result, we
have obtained approximately 92% judgment accuracy.

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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom