arXiv Open Access 2021

Ad Text Classification with Transformer-Based Natural Language Processing Methods

Umut Özdil Büşra Arslan D. Emre Taşar Gökçe Polat Şükrü Ozan

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (5)

Umut Özdil

Büşra Arslan

D. Emre Taşar

Gökçe Polat

Şükrü Ozan

Format Sitasi

APA MLA BibTeX

Özdil, U., Arslan, B., Taşar, D.E., Polat, G., Ozan, Ş. (2021). Ad Text Classification with Transformer-Based Natural Language Processing Methods. https://arxiv.org/abs/2106.10899

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓