A Stacked Graphical Model for Associating Sub-Images with Sub-Captions
A Stacked Graphical Model for Associating Sub-Images with Sub-Captions
复制标题
用于将子图像与子标题相关联的堆叠图形模型
DOI：
10.1142/9789812772435_0025
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发表时间：
2006
影响因子：
--
通讯作者：
R. Murphy
中科院分区：
文献类型：
--
作者：
Zhenzhen Kou;William W. Cohen;R. Murphy
人们对从全文中挖掘数据有着广泛的兴趣。我们建立了一个名为SLIF（亚细胞定位图像查找器）的系统，它可以从期刊文章的文本和图像组合中提取生物学某一方面的信息。从文本和图像中关联信息需要将子图与文本中的句子相匹配。我们引入了一种堆叠图形模型，这是一种元学习方案，通过基于相关实例扩展特征来增强基础学习器，从而将子图的标签与句子的标签相匹配。实验结果表明，与关系依赖网络（70.8%）或SLIF中当前算法（64.3%）相比，堆叠图形模型的匹配精度（81.3%）有显著提高。
There is extensive interest in mining data from full text. We have built a system called SLIF (for Subcellular Location Image Finder), which extracts information on one particular aspect of biology from a combination of text and images in journal articles. Associating the information from the text and image requires matching sub-figures with the sentences in the text. We introduce a stacked graphical model, a meta-learning scheme to augment a base learner by expanding features based on related instances, to match the labels of sub-figures with labels of sentences. The experimental results show a significant improvement in the matching accuracy of the stacked graphical model (81.3%) as compared with a relational dependency network (70.8%) or the current algorithm in SLIF (64.3%).