Ad Text Classification with Transformer-Based Natural Language Processing Methods
Computation and Language
2021-06-24 v2
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.
Cite
@article{arxiv.2106.10899,
title = {Ad Text Classification with Transformer-Based Natural Language Processing Methods},
author = {Umut Özdil and Büşra Arslan and D. Emre Taşar and Gökçe Polat and Şükrü Ozan},
journal= {arXiv preprint arXiv:2106.10899},
year = {2021}
}
Comments
6 pages, in Turkish language, 4 figures, 3 tables