Abstract
Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.
Highlights
Nowadays, information available on the World Wide Web (WWW) is growing exponentially, due to which finding user-relevant web pages has become challenging and tedious
This paper proposed and implemented a model for web page categorization that utilized the GloVe and Stacked bidirectional Long Short Term Memory (LSTM) (BiLSTM)
Feature extraction and classifier design are crucial processes to achieve this task, and many machine learning models have shown a better performance in this field
Summary
Information available on the World Wide Web (WWW) is growing exponentially, due to which finding user-relevant web pages has become challenging and tedious. A search engine either returns too many results or misinterprets the user query due to linguistic ambiguity [1]. Earlier research approaches have addressed the problem of web page classification as per the user’s preferences [15]. This is a simple document classification problem based on the textual contents and features of web pages. The classification of web pages is based on counting the frequency of text terms to form a term frequency feature vector. These feature vectors are applied to train the classifier to classify web pages. Feature vectors extracted from the title and main text of the web pages were utilized by the naive Bayesian classifier
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