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    • 1. 发明授权
    • High precision set expansion for large concepts
    • 高精度集扩展为大概念
    • US09547718B2
    • 2017-01-17
    • US13325072
    • 2011-12-14
    • Jiewen HuangZhimin ChenArvind ArasuVivek Narasayya
    • Jiewen HuangZhimin ChenArvind ArasuVivek Narasayya
    • G06F17/30
    • G06F17/30867G06Q30/0201
    • A set expansion system is described herein that improves precision, recall, and performance of prior set expansion methods for large sets of data. The system maintains high precision and recall by 1) identifying the qualify of particular lists and applying that quality through a weight, 2) allowing for the specification or negative examples in a set of seeds to reduce the introduction of bad entities into the set, and 3) applying a cutoff to eliminate lists that include a low number of positive matches. The system may perform multiple passes to first generate a good candidate result set and then refine the set to find a set with highest quality. The system may also apply Map Reduce or other distributed processing techniques to allow calculation in parallel. Thus, the system efficiently expands large concept sets from a potentially small set of initial seeds from readily available web data.
    • 本文描述了一种扩展系统,可提高大型数据集的先前设置扩展方法的精度,调用和性能。 该系统通过1)确定特定列表的资格并通过权重来应用该质量,保持高精度和召回; 2)允许一组种子中的规范或否定示例,以减少将不良实体引入到集合中; 3)应用截止值来消除包括少量正匹配的列表。 系统可以执行多次通过以首先产生良好的候选结果集合,然后对该集合进行优化以找到具有最高质量的集合。 该系统还可以应用Map Reduce或其他分布式处理技术来并行计算。 因此,系统从容易获得的网络数据的一小部分初始种子中有效地扩展了大概念集。
    • 3. 发明申请
    • HIGH PRECISION SET EXPANSION FOR LARGE CONCEPTS
    • 高精度扩展大概念
    • US20130159317A1
    • 2013-06-20
    • US13325072
    • 2011-12-14
    • Jiewen HuangZhimin ChenArvind ArasuVivek Narasayya
    • Jiewen HuangZhimin ChenArvind ArasuVivek Narasayya
    • G06F17/30
    • G06F17/30867G06Q30/0201
    • A set expansion system is described herein that improves precision, recall, and performance of prior set expansion methods for large sets of data. The system maintains high precision and recall by 1) identifying the qualify of particular lists and applying that quality through a weight, 2) allowing for the specification or negative examples in a set of seeds to reduce the introduction of bad entities into the set, and 3) applying a cutoff to eliminate lists that include a low number of positive matches. The system may perform multiple passes to first generate a good candidate result set and then refine the set to find a set with highest quality. The system may also apply Map Reduce or other distributed processing techniques to allow calculation in parallel. Thus, the system efficiently expands large concept sets from a potentially small set of initial seeds from readily available web data.
    • 本文描述了一种扩展系统,可提高大型数据集的先前设置扩展方法的精度,调用和性能。 该系统通过1)确定特定列表的资格并通过权重来应用该质量,保持高精度和召回; 2)允许一组种子中的规范或否定示例,以减少将不良实体引入到集合中; 3)应用截止值来消除包括少量正匹配的列表。 系统可以执行多次通过以首先产生良好的候选结果集合,然后对该集合进行优化以找到具有最高质量的集合。 该系统还可以应用Map Reduce或其他分布式处理技术来并行计算。 因此,系统从容易获得的网络数据的一小部分初始种子中有效地扩展了大概念集。