Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
SmallBlue: People Mining for Expertise Search
IEEE MultiMedia
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Focused on the problem of numerous labeling works on the expert homepage in the procedure of Chinese expert entity homepage recognition, in this paper, a method of Chinese expert entity homepage recognition based on the Co-EM proposed. In detail, firstly, collect the names of Chinese expert entity and the corresponding web pages, and then label a small quantity of web pages. Secondly for Chinese entity characteristics, extract the hyperlink features and the web page content features as two independent feature sets. Thirdly train the hyperlink classifier using the hyperlink feature set and label the all the expert entity homepages, and then train the content classifier using the web page content feature set and the labels which were labeled by the hyperlink classifier. Use the labels which were labeled by the content classifier to update the hyperlink classifier. Repeat the procedure until the two classifiers converge. Finally, experiments were done by employing the method of 10-fold cross validation. The results show that the method based on the Co-EM semisupervised algorithm can uses the unlabeled web pages effectively and there is an increase of accuracy of recognition compared with using the labeled web pages only.