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Academic Network Repetitions disambiguation algorithm

Author: LinQuan
Tutor: LiYuHua
School: Huazhong University of Science and Technology
Course: Applied Computer Technology
Keywords: Repetitions disambiguation Academic Network Feature Extraction Constraint User feedback
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 0
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Abstract


Cooperation networks among scientists , there are many scientists names are the same. Currently renowned academic platforms such as Arnetminer, Springer, ACM, DBLP, CiteSeer other scientists in academic ability on statistics when it comes to the names of scientists to distinguish between scientists , resulting in a large number of statistical error , but also to bring research scientists representing cooperative network large deviations , so the same name disambiguation problem of great significance . Repetitions disambiguation existing feature selection algorithms are mainly concentrated on the co-author of the reference relationship , Author , etc., in the choice of the model is mainly graph model , there is precision and recall rate is not high. By analyzing induction in handling the same name disambiguation method used when the problem will be the same name disambiguation determines the clustering problem into whether one of the two academic classification problems . Predecessors in the absorption and improved handling Repetitions disambiguation problem , based on the extracted features , proposed some new features: co-author (Co-Author), Home (Homepage), the reference relationship (Citation), Author (Co -Org), the title similarity (Titile-Similariy), search engines (Digital-Lib), literature text (PDF File). Using perceptron as classifier, as a constraint on the use of personal home page Perceptron classification results to be amended. To further improve the accuracy of the same name disambiguation algorithm , feedback information into account . According to the credibility of user feedback , the feedback classify trusted user feedback from low to extract features added to the perceptron input , select a high- trusted user feedback as an additional constraint to fix Perceptron output , the user feedback perceptron as the training set for ongoing training , constantly correct perceptron . Experimental results show that the introduction of user feedback after the exact same name disambiguation algorithm performance has been significantly improved , and achieved good results , the current algorithm has been used in Arnetminer system .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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