An Improved Centroid-Based Approach for Multi-label Classification of Web Pages by Genre

2011 ◽  
Author(s):  
Chaker Jebari
Keyword(s):  
Web Pages

Classification of Spamming Attacks to Blogging Websites and Their Security Techniques

2020 ◽  
pp. 864-880 ◽  
Author(s):  
Rizwan Ur Rahman ◽  
Rishu Verma ◽  
Himani Bansal ◽  
Deepak Singh Tomar
Keyword(s):  
Search Engine ◽  
World Wide ◽  
Web Search ◽  
Service Providers ◽  
Web Pages ◽  
Internet Service ◽  
Important Concern ◽  
Attack Scenario ◽  
Explosive Expansion

With the explosive expansion of information on the world wide web, search engines are becoming more significant in the day-to-day lives of humans. Even though a search engine generally gives huge number of results for certain query, the majority of the search engine users simply view the first few web pages in result lists. Consequently, the ranking position has become a most important concern of internet service providers. This article addresses the vulnerabilities, spamming attacks, and countermeasures in blogging sites. In the first part, the article explores the spamming types and detailed section on vulnerabilities. In the next part, an attack scenario of form spamming is presented, and defense approach is presented. Consequently, the aim of this article is to provide review of vulnerabilities, threats of spamming associated with blogging websites, and effective measures to counter them.

Automatic Classification of Tibetan Web Pages

2012 ◽  
Author(s):  
Guixian Xu ◽  
Chuncheng Xiang ◽  
Xu Gao ◽  
Xiaobing Zhao ◽  
Guosheng Yang
Keyword(s):  
Automatic Classification ◽  
Web Pages

A Method of Subtopic Classification of Search Engine Suggests by Integrating a Topic Model and Word Embeddings

2018 ◽  
Vol 6 (3) ◽  
pp. 67-78
Author(s):  
Tian Nie ◽  
Yi Ding ◽  
Chen Zhao ◽  
Youchao Lin ◽  
Takehito Utsuro
Keyword(s):  
Search Engine ◽  
Information Needs ◽  
Web Search ◽  
Topic Model ◽  
Japanese Version ◽  
Word Embedding ◽  
Coarse Grained ◽  
Web Pages ◽  
Word Embeddings

The background of this article is the issue of how to overview the knowledge of a given query keyword. Especially, the authors focus on concerns of those who search for web pages with a given query keyword. The Web search information needs of a given query keyword is collected through search engine suggests. Given a query keyword, the authors collect up to around 1,000 suggests, while many of them are redundant. They classify redundant search engine suggests based on a topic model. However, one limitation of the topic model based classification of search engine suggests is that the granularity of the topics, i.e., the clusters of search engine suggests, is too coarse. In order to overcome the problem of the coarse-grained classification of search engine suggests, this article further applies the word embedding technique to the webpages used during the training of the topic model, in addition to the text data of the whole Japanese version of Wikipedia. Then, the authors examine the word embedding based similarity between search engines suggests and further classify search engine suggests within a single topic into finer-grained subtopics based on the similarity of word embeddings. Evaluation results prove that the proposed approach performs well in the task of subtopic classification of search engine suggests.

Dynamic and hierarchical classification of Web pages

2004 ◽  
Vol 28 (2) ◽  
pp. 139-147 ◽  
Author(s):  
Ben Choi ◽  
Xiaogang Peng
Keyword(s):  
Hierarchical Classification ◽  
Web Pages
Sign in / Sign up

Export Citation Format

Share Document