Abstract
Currently, the internet is growing at an exponential rate and can cover just some required data. However, the immense amount of web pages makes the discovery of the target data more difficult for the user. Therefore, an efficient method to classify this huge amount of data is essential where web pages can be exploited to their full potential. In this paper, we propose an approach to classify Web pages based on their textual content. This approach is based on an unsupervised statistical technique (TF-IDF) for keyword extraction (textual content) combined with a supervised machine learning approach, namely recurrent neural networks.
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