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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
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
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