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9th International Conference on Ubiquitous Networking (UNet)

作者： Abdelmounaim, Kerkri Madani, Mohamed Amine Wiam, Rabhi Belaouchi, Lamyae Oumayma, El Fahsi Mohammed Premier Univ Oujda Natl Sch Appl Sci Lab Stochast & Determinist Modeling Oujda Morocco Mohammed Premier Univ Natl Sch Appl Sci Engn Sci Lab LSI Oujda Morocco Mohammed Premier Univ Natl Sch Appl Sci Oujda Morocco

ISBN: (纸本)9783031624872;9783031624889

Given the growing volume of online content, web page classification is crucial for ensuring accurate and efficient information retrieval. In this study, we explore several sentence embedding techniques, based on transfer learning models, in the context of a web page classification task. We further compare three of these sentence embedding techniques, with three other LSTM architectures: a vanilla LSTM model, bidirectional LSTM (BiLSTM) and LSTM with attention mechanism. The dataset we used contains roughly 11000 web pages that have been legally scraped from a well known news website. The comparison shows that BERT sentence embedding outperforms all models, including the LSTM hybrid model with attention mechanism.

关键词： BERT GloVe USE. LSTM web page classification

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Deep Learning Approaches for web page classification in Focused Crawling 3

Deep Learning Approaches for Web Page Classification in Focu...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Dhabliya, Dharmesh Al-Fatlawy, Mohammed Hussien Kumhar, Bhupchand Rajeswari, M. Sundaram, N. Kalyana Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies University Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

web page classification plays a crucial role in focused crawling, where the goal is to selectively retrieve relevant pages based on specific topics or domains of interest. Deep learning approaches have gained significant attention in recent years due to their ability to extract high-level representations from raw data. This paper presents an overview of deep learning techniques used for web page classification in focused crawling. We discuss various deep learning architectures, training strategies, and evaluation metrics employed in this context. Furthermore, we provide insights into the challenges and future directions in this rapidly evolving field. © 2023 IEEE.

关键词： Deep learning focused crawling natural language processing neural networks web page classification

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web page classification: a survey of perspectives, gaps, and future directions

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第17-18期79卷 11921-11945页

作者： Hashemi, Mahdi George Mason Univ Dept Informat Sci & Technol 4400 Univ Dr Fairfax VA 22030 USA

The explosive growth of the amount of information on Internet has made web page classification essential for web information management, retrieval, and integration, web page indexing, topic-specific web crawling, topic-specific information extraction models, advertisement removal, filtering out unwanted, futile, or harmful contents, and parental control systems. Owing to the recent staggering growth of performance and memory space in computing machines, along with specialization of machine learning models for text and image classification, many researchers have begun to target the web page classification problem. Yet, automatic web page classification remains at its early stages because of its complexity, diversity of web pages' contents (images of different sizes, text, hyperlinks, etc.), and its computational cost. This paper not only surveys the proposed methodologies in the literature, but also traces their evolution and portrays different perspectives toward this problem. Our study investigates the following: (a) metadata and contextual information surrounding the terms are mostly ignored in textual content classification, (b) the structure and distribution of text in HTML tags and hyperlinks are understudied in textual content classification, (c) measuring the effectives of features in distinguishing among web page classes or measuring the contribution of each feature in the classification accuracy is a prominent research gap, (d) image classification methods rely heavily on computationally intensive and problem-specific analyses for feature extraction, (e) semi-supervised learning is understudied, despite its importance in web page classification because of the massive amount of unlabeled web pages and the high cost of labeling, (f) deep learning, convolutional and recurrent networks, and reinforcement learning remain underexplored but intriguing for web page classification, and last but not least (g) developing a detailed testbed along with evaluation metric

关键词： web page classification Image classification Text classification Deep learning Machine learning Artificial intelligence

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Ensemble approach for web page classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第16期80卷 25219-25240页

作者： Gupta, Amit Bhatia, Rajesh Deemed Univ Punjab Engn Coll Dept Comp Sci & Engn Chandigarh India

Over the decades World Wide web has become abundance source of distributed web content repository hyper-linked with diverse information domains. Performance of search engines in locating the information is exemplary but still there is inadequacy in search engines for focused crawling of web content. web page classification being pivotal for information retrieval and management task plays imperative role for natural language processing in creating classified web document repositories and building indexed web directories. The conventional machine learning approaches extract the desired features from web pages in order to classify them whereas deep leaning algorithms learns the covet features as the network goes deeper and deeper. Transfer learning based Pre-trained models such as BERT attains impressive performance for text classification. In this study, we evaluate the effectiveness of adopting pre-trained model BERT for the task of classifying web pages into different categories. In this paper, we proposed an ensemble approach for web page classification by learning contextual representation using pre-trained bidirectional BERT and then applying deep Inception modelling with Residual connections for fine-tunes the target task by utilizing parallel multi-scale semantics. Experimental evaluation exhibit that proposed ensemble model outperforms benchmark baselines and achieve better performance in contrast to other transfer learning approaches evaluated on the web page classification task for different classification datasets.

关键词： web page classification Transfer learning Residual inception modelling Pre-trained model

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Knowledge Based Deep Inception Model for web page classification

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JOURNAL OF web ENGINEERING 2021年第7期20卷 2131-2167页

作者： Gupta, Amit Bhatia, Rajesh Punjab Engn Coll Deemed Univ Dept Comp Sci & Engn Chandigarh India

web page classification is decisive for information retrieval and management task and plays an imperative role for natural language processing (NLP) problems in web engineering. Traditional machine learning algorithms excerpt covet features from web pages whereas deep leaning algorithms crave features as the network goes deeper. Pre-trained models such as BERT attains remarkable achievement for text classification and continue to show state-of-the-art results. Knowledge Graphs can provide rich structured factual information for better language modelling and representation. In this study, we proposed an ensemble Knowledge Based Deep Inception (KBDI) approach for web page classification by learning bidirectional contextual representation using pre-trained BERT incorporating Knowledge Graph embeddings and fine-tune the target task by applying Deep Inception network utilizing parallel multi-scale semantics. Proposed ensemble evaluates the efficacy of fusing domain specific knowledge embeddings with the pre-trained BERT model. Experimental interpretation exhibit that the proposed BERT fused KBDI model outperforms benchmark baselines and achieve better performance in contrast to other conventional approaches evaluated on web page classification datasets.

关键词： web page classification transfer learning knowledge graph embedding pre-trained model

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web page classification based on heterogeneous features and a combination of multiple classifiers

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Frontiers of Information Technology & Electronic Engineering 2020年第7期21卷 995-1004页

作者： Li DENG Xin DU Ji-zhong SHEN College of Information Science&Electronic Engineering Zhejiang UniversityHangzhou 310027China

Precise web page classification can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a web page classification method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags, we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies, such as voting, confidence comparison, and direct output, to give the final classification results. Experimental results demonstrate that on the Amazon dataset, 7-web-genres dataset, and DMOZ dataset, the accuracies are increased to 94.2%, 95.4%, and 95.7%, respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach, and the accuracy is higher than that when using only textual features. At the same time, the accuracy of the web page classification is improved by combining multiple classifiers, and is higher than those of the related web page classification algorithms.

关键词： web page classification web page features Combined classifiers

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Stemming Text-based web page classification using Machine Learning Algorithms: A Comparison

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第1期11卷 570-576页

作者： Razali, Ansari Daud, Salwani Mohd Zin, Nor Azan Mat Shahidi, Faezehsadat Univ Teknol Malaysia Dept Informat FTIR Kuala Lumpur Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi Selangor Malaysia

The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in term of accuracy and processing time. This research uses BBC dataset that has five predefined categories. The result demonstrates that classifiers' performance is better without word stemming, whereby all classifiers show higher classification accuracy, with the highest accuracy produced by NB and SVM at 97% for F1 score, while NB takes shorter training time than SVM. With word stemming, the effect on training and classification time is negligible, except on Multilayer Perceptron in which word stemming has effectively reduced the training time.

关键词： web page classification stemming machine learning Naive Bayes k-NN SVM multilayer perceptron

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web page classification Method Based on Semantics and Structure 2

Web Page Classification Method Based on Semantics and Struct...

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2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Li, Huaxin Zhang, Zhaoxin Xu, Yongdong Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781728108315

Network information has got explosive growth on because of the rapid development of Internet technology. web page classification technology can solve the problem of information clutter to a large extent, which is widely used in search engines, digital libraries and information retrieval. web page classification is different from text classification, because web page is a kind of semi-structured data that not only contains text semantic information, but also has rich structural information. This paper analyzes and utilizes the structural characteristics of web pages. Thus, we propose a web page classification method focused on semantics and structure joint features, and train on top of convolution neural networks model. The results show that using joint features of structure and semantics information plays an important role in classification accuracy, which has increased F1 value of web page classification by 4%-6%.

关键词： web page classification joint features convolutional neural networks structure semantics

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web page classification Using RNN 9th

Web Page Classification Using RNN

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9th International Conference of Information and Communication Technology [ICICT]

作者： Buber, Ebubekir Diri, Banu Yildiz Tech Univ Comp Engn Dept Istanbul Turkey

web page classification is an information retrieval application that provides useful information that can be a basis for many different application domains. In this study, a deep learning-based system has been developed for the classification of web pages. The meta tag information contained in the web page is used to classify a web page. The meta tags used are title, description and keywords. RNN based deep learning architecture was used during the tests. Transfer learning is the name given to the approach to building a machine learning model with the use of pre-trained parameters to solve a problem. The effect of using transfer learning on the system has also been examined. According to the results obtained, success rate of web page classification system is approximately 85%. It is not observed that transfer learning has significant contribution to the success rates. However, the use of transfer learning has reduced the consumed system resources. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： web page classification classification categorization deep learning RNN transfer learning

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web page classification Using RNN

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Procedia Computer Science 2019年 154卷 62-72页

作者： Ebubekir Buber Banu Diri

关键词： web page classification classification categorization deep learning RNN transfer learning

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