Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ad Text Classification with Transformer-Based Natural Language
  Processing Methods   In this study, a natural language processing-based (NLP-based) method is
proposed for the sector-wise automatic classification of ad texts created on
online advertising platforms. Our data set consists of approximately 21,000
labeled advertising texts from 12 different sectors. In the study, the
Bidirectional Encoder Representations from Transformers (BERT) model, which is
a transformer-based language model that is recently used in fields such as text
classification in the natural language processing literature, was used. The
classification efficiencies obtained using a pre-trained BERT model for the
Turkish language are shown in detail.

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The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
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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: math
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con