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Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
information sorting ability and problem-solving ability. It also implies that students built an information organizing model by using classified Social Bookmarking of network knowledge to assist project-based inquiry learning. The internalized model starts from information needs, clarifying main topic, searching keywords
plus noun phrase learning for extraction of activity concepts in Chinese. We also propose an algorithm of relevance measurement for extracting relation instances by binary keywords based on co-occurrence statistics. Finally, we build a practical system of ontology learning through learning relation instances of the
Social tagging allows users to assign keywords (tags) to resources facilitating their future access by the tag creator, and possibly by other users. In terms of its support for resource discovery, social tagging has both proponents and critics. The goal of this paper investigates if tags are an effective means for
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
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
construction approach based on a modified TF-IDF, the ATF-IDF, and the well-known formal concept analysis, the FCA, algorithms. First, the approach constructs a concept lattice using keywords extracted by the ATF-IDF from collected documents to form a relationship hierarchy between all the concepts represented by the keywords. It
recursive partitioning ranking scheme, are capable of reverse engineering Google's ranking algorithm with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78% of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10
automatically constructs a navigational structure for the WWW to help information finding. A self-organizing map is constructed to train the Web pages and obtain two feature maps, which reveal the relationships among Web pages and thematic keywords respectively. We then use these maps to develop a structure that may assist the
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