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Ad Text Classification with Transformer-Based Natural Language Processing Methods

2021-06-21 07:38:31
Umut Özdil, Büşra Arslan, D. Emre Taşar, Gökçe Polat, Şükrü Ozan
arXiv_CL
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.10899

PDF

https://arxiv.org/pdf/2106.10899.pdf

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