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Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content
overloaded sites for a short piece of information of their interest. The crawler developed in the system gathers web page information which is processed using Natural Language Processing and Procedure programming for a specific keyword. The system returns precise short string answers or list to natural language questions
content-specific CPs and a methodology to extract content-specific CPs from click-through data, given keywords describing every web page. Content-specific CP is a set of web pages visited by certain percentage of users interested in particular content of the web pages. Experimental results show that the proposed model
order to collect and index only related Web documents. As requests can be insufficient to express sensitive and specific needs, the user's information needs are specified using user's interests represented by DBPedia resources [1] and keywords, both extracted from Web pages provided by the user. A series of experiments
algorithms, web image information is extracted from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text. However, the systems that attempt to mine information for images using surrounding text suffer from several problems, such as the inability to correctly assign all
information just searching search engine like Google and Baidu with keywords and browseWeb pages selecting useful information, people want to get interested knowledge continuously through pushing technology. Recommendation is of great significance in knowledge discovery. Recommender systems typically produce a list of
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
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