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search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children's) websites, and (iii
We propose an intuitive operation based information retrieval system “Clickable Real World (CRW)”. If a user which uses this system takes a picture of a landmark in the world, some related information is displayed on a smartphone. One of the key research issues is how to estimate appropriate keywords in
for developing an efficient political chatterbot. We set our study in the context of 2016 Brexit referendum. We argue that employing a subjectivity detector and an emotion analyzer, in addition to the keyword based topic detector, enhances the intent detection process. Next, we discuss the importance of maintaining
Nonlinear Auto-Regressive Moving Average (NARMA)) to forecast Malaysian tourism influx based on the volume of internet searches of the keyword ‘tourism Malaysia’ in Google Trends, based on proven strong correlatedness between the volume of internet searches with tourism in a particular area. Both models were
(keyword), we collect a large amount of object-related images from two main image sources: Google Images and the LabelMe website. We deal with the problem of separating good training samples from noisy images by performing two steps: similar image selection and non-real image filtering. We use a variant of Gaussian
commercial web search engines, a large fraction of returned images is not related to the query keyword. We present a SVM based active learning approach to selecting relevant images from noisy image search results. The resulting database is more diverse with more sample images, compared with other well established facial
Most web search engines use only the search keywords for searching. Due to the ambiguity of semantics and usages of the search keywords, the results are noisy and many of them do not match the user's search goals. This paper presents the design of an intelligent Search Bot, which operates as an agent for a user by
robustness checks considering alternative Google search keywords, the potential effects of the recession between 2008 and 2009 and the inclusion of the two dimensions of the Dynamic-Weighted Nominate (DWN) database.
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