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Extracting Author Meta-Data from Web Using Visual Features

9 years 4 months ago
Extracting Author Meta-Data from Web Using Visual Features
Enriching digital library’s author meta-data can lead to valuable services and applications. This paper addresses the problem of extracting authors’ information from their homepages. This problem is actually a multiclass classification problem. A homepage can be treated as a group of information pieces which need to be classified to different fields, e.g., Name, Title, Affiliation, Email, etc. In this problem, not only each information piece can be viewed as a point in a feature space, but also certain patterns can be observed among different fields on a page. To improve the extraction accuracy, this paper argues that visual features of information pieces on a homepage should be sufficiently utilized. In addition, this paper also proposes an inter-fields probability model to capture the relation among different fields. This model can be combined with featurespace based classification. Experimental results demonstrate that utilizing visual features and applying the inter...
Shuyi Zheng, Ding Zhou, Jia Li, C. Lee Giles
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICDM
Authors Shuyi Zheng, Ding Zhou, Jia Li, C. Lee Giles
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