Using BERT for Multi-Label Multi-Language Web Page Classification
Codruț-Georgian Artene, Marius Nicolae Tibeică, Florin Leon · 2021
With a very large and constantly growing number of web pages available on the Internet and considering the diversity of the topics that the content of web pages develops, automatic web page classification is becoming increasingly important. The text represents one of the main components of the content of web pages. In the last years, researchers have achieved state-of-the-art results in various natural language processing tasks, including text classification. This was possible mainly due to the development of language models that are trained on large text corpora and afterwards fine-tuned for specific tasks in a transfer learning manner. Such a model is Bidirectional Encoder Representations from Transformers (BERT), which has proven to be very effective for text classification. In this work, we propose a series of experiments to evaluate the effectiveness of the pre-trained multilingual BERT on multi-label multi-language web page classification. Overall, our proposed web page classifier achieves competitive results and one may conclude that it can be part of an automatic web page classification system.