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    • 5. 发明授权
    • Generating Chinese language banners
    • 生成中文横幅
    • US08862459B2
    • 2014-10-14
    • US13087407
    • 2011-04-15
    • Long JiangMing ZhouSu Hao
    • Long JiangMing ZhouSu Hao
    • G06F17/27G06F17/24G06F17/21G06F17/28
    • G06F17/21G06F17/24G06F17/2863
    • Embodiments are disclosed for automatically generating a banner given a first scroll sentence and a second scroll sentence of a Chinese couplet. The first and/or second scroll sentence can be generated by an automatic computer system or by a human (e.g., manually generated and then provided as input to an automated banner generation system) or obtained from any source (e.g., a book) and provided as input. In one embodiment, an information retrieval process is utilized to identify banner candidates that best match the first and second scroll sentences. In one embodiment, candidate banners are automatically generated. In one embodiment, a ranking model is applied in order to rank banner candidates derived from the banner search and generation processes. One or more banners are then selected from the ranked banner candidates.
    • 公开了用于自动生成给定中文对联的第一滚动句和第二滚动句的横幅的实施例。 第一和/或第二滚动句可以由自动计算机系统或人(例如,手动生成然后作为自动横幅生成系统的输入提供)或从任何来源(例如,书)获得并提供 作为输入。 在一个实施例中,使用信息检索处理来识别与第一和第二滚动句子最匹配的横幅候选。 在一个实施例中,自动生成候选横幅。 在一个实施例中,应用排序模型以排序从横幅搜索和生成处理导出的横幅候选。 然后从排名的横幅候选中选择一个或多个横幅。
    • 8. 发明授权
    • Joint ranking model for multilingual web search
    • 多语言网络搜索的联合排名模型
    • US08326785B2
    • 2012-12-04
    • US12241078
    • 2008-09-30
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • G06F17/00G06F17/20G06F7/00G06F17/30G06N5/00
    • G06F17/30675
    • A classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
    • 构建分类器至少部分地基于与其他文档的相似性以及这些其他文档与查询的相关性来对查询中发现的不同语言的文档进行排序。 联合排名模型,例如基于玻尔兹曼(Boltzmann)机器,用于表示文档之间的内容相似性,并且帮助确定一组文档的联合相关概率。 因此,利用一种语言的相关文件来改进不同语言文件的相关性估计。 在一个方面,隐藏的单位(神经元)表示检索的文档中的集群(对应于相关主题),输出层表示相关文档及其特征,边缘表示集群和文档之间的关系。