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In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set...
party servers. In this paper, we discuss on the authenticated search results of some recent works and then present an improved scheme that ensures the authenticity of the search results corresponding to a search query over Internet. The improved scheme is based on the scheme [1] that uses the concept of conjunctive keyword
propose a """"Hybrid Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions"""" (""""IoT-SVK Search Engine"""" for short). The experimental results
An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the hidden Web or the deep Web. Since there are no static links
Given a set of keywords, we find a maximum Web query (containing the most keywords possible) that respects user-defined bounds on the number of returned hits. We assume a real-world setting where the user is not given direct access to a Web search engine's index, i.e., querying is possible only through an interface
The success of the search engine may be our Newtonian paradigm for the Web. It enables us to do so much information discovery that it is difficult to imagine what we cannot do with it.
Language Model (LM) constitutes one of the key components in Keyword Spotting (KWS). The rapid development of the World Wide Web (WWW) makes it an extremely large and valuable data source for LM training, but it is not optimal to use the raw transcripts from WWW due to the mismatch of content between the web corpus
Search engines award their advertising space through keyword auctions. Some bidders may adopt an aggressive bidding strategy known as Competitor Busting, where they submit higher bids than what is strictly needed to win the auction so as to oust the other bidders. Despite the widespread concern for such practice, we
issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has
Processing short texts is becoming a trend in information retrieval. Since the text has rarely external information, it is more challenging than document. In this paper, keyword clustering is studied for automatic categorization. To obtain semantic similarity of the keywords, a broad-coverage lexical resource WordNet
integrate information from multiple interrelated pages to answer keyword queries meaningfully. Next-generation web search engines require link-awareness, or more generally, the capability of integrating correlative information items that are linked through hyperlinks. In this paper, we study the problems of identifying the
Keyword auctions are being used to sell the positions along the side of organic results shown by search engine when user types a keyword or a query related to keyword in a search engine. It has been a huge revenue generating arena for search engines since last decade. Irrespective of the great success of these types
Search engines including Yahoo! and Google utilize a keyword auction for ranking the advertisements displayed around the search results. In existing keyword auctions called the GSP, the number of displayed advertisements (slots) is determined in advance. Therefore, we consider adjusting the number of advertisements
Search engines on the Web have popularized the keyword-based search paradigm, while searching in databases users need to know a database schema and a query language. Keyword search techniques on the Web cannot directly be applied to databases because the data on the Internet and database are in different forms
Search engines on the Web have popularized the keyword-based search paradigm, while searching in databases users need to know a database schema and a query language. Keyword search techniques on the Web cannot directly be applied to databases because the data on the Internet and database are in different forms. So
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer
quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword and ”, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is
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