| 专家证据文档识别无向图模型 |
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| 引用本文： | 毛存礼,余正涛,吴则建,郭剑毅,线岩团.专家证据文档识别无向图模型[J].软件学报,2013,24(11):2734-2746. |
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| 作者姓名： | 毛存礼 余正涛 吴则建 郭剑毅 线岩团 |
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| 作者单位： | 昆明理工大学 信息工程与自动化学院, 云南 昆明 650500;昆明理工大学 信息工程与自动化学院, 云南 昆明 650500;昆明理工大学 信息工程与自动化学院, 云南 昆明 650500;昆明理工大学 信息工程与自动化学院, 云南 昆明 650500;昆明理工大学 信息工程与自动化学院, 云南 昆明 650500 |
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| 基金项目： | 国家自然科学基金(61175068);教育部留学回国人员启动基金;云南省教育厅科研基金重大专项;云南省软件工程重点实验室开放基金(2011SE14) |
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| 摘 要： | 专家证据文档识别是专家检索的关键步骤.融合专家候选文档独立页面特征以及页面之间的关联关系,提出了一个专家证据文档识别无向图模型.该方法首先分析各类专家证据文档中的词、URL 链接、专家元数据等独立页面特征以及候选专家证据文档间的链接和内容等关联关系;然后将独立页面特征以及页面之间的关联关系融入到无向图中构建专家证据文档识别无向图模型;最后利用梯度下降方法学习模型中特征的权重,并利用吉布斯采样方法进行专家证据文档识别.通过对比实验验证了该方法的有效性.实验结果表明,该方法有较好的效果.
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| 关 键 词： | 专家证据文档 专家检索 独立页面特征 专家元数据 无向图模型 |
| 收稿时间： | 5/6/2013 12:00:00 AM |
| 修稿时间： | 8/2/2013 12:00:00 AM |
Undirected Graph Model for Expert Evidence Document Recognition |
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| MAO Cun-Li,YU Zheng-Tao,WU Ze-Jian,GUO Jian-Yi and XIAN Yan-Tuan.Undirected Graph Model for Expert Evidence Document Recognition[J].Journal of Software,2013,24(11):2734-2746. |
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| Authors: | MAO Cun-Li YU Zheng-Tao WU Ze-Jian GUO Jian-Yi and XIAN Yan-Tuan |
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| Affiliation: | School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China |
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| Abstract: | Expert evidence document recognition is the key step for expert search. Combining specialist candidate document independent page features and correlation among pages, this paper proposes an expert evidence document recognition method based on undirected graph model. First, independent page features such as words, URL links and expert metadata in all kinds of expert evidence document, and correlations such as links and content among candidate expert evidence document are analyzed. Then, independent page features and correlation among pages are integrated into the undirected graph to construct an undirected graph model for expert evidence document recognition. Finally, feature weights are learned in the model by using the gradient descent method and expert evidence document recognition is achieved by utilizing Gibbs Sampling method. The effectiveness of the proposed method is verified by comparison experiment. The experimental results show that the proposed method has a better effect. |
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| Keywords: | expert evidence document expert search independent page feature expert metadata undirected graph model |
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