Alleles in the HtrA Serine Peptidase 1 Gene Alter the Risk of Neovascular Age-Related Macular Degeneration
- 作者：Margaret M. DeAngelis ; Fei Ji ; Scott Adams ; Margaux A. Morrison ; Am ; a J. Harring ; Meredith O. Sweeney ; Antonio Capone Jr ; Joan W. Miller ; Thaddeus P. Dryja ; Jurg Ott ; Ivana K. Kim
- 关键词：Semantic relation extraction ; Data sparseness problem ; Hierarchical learning strategy ; Class hierarchy ; Flat learning strategy
- 刊名：Ophthalmology
- 出版年：2008
- 期刊代码：211_01616420
- 类别：med
- 出版时间：July 2008
- 卷：115
- 期：7
- 页码：1209-1215.e7
- 文件大小：396 K
摘要
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and guide the discriminative function learning in the lower-level one more effectively, which otherwise might suffer from limited training data. In this paper, two classifier learning approaches, i.e. the simple perceptron algorithm and the state-of-the-art Support Vector Machines, are applied using the hierarchical learning strategy. Moreover, several kinds of class hierarchies either manually predefined or automatically clustered are explored and compared. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.