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    • 99. 发明申请
    • System And Method For Managing User Attention By Detecting Hot And Cold Topics In Social Indexes
    • 通过检测社会指标中的热门和冷静话题来管理用户注意力的系统和方法
    • US20100191742A1
    • 2010-07-29
    • US12360834
    • 2009-01-27
    • Mark J. StefikSanjay MittalLance E. Good
    • Mark J. StefikSanjay MittalLance E. Good
    • G06F17/30
    • G06F17/30867G06F17/30705G06F17/30707G06F17/3071G06F17/30713
    • A system and method for managing user attention by detecting hot topics in social indexes is provided. Articles of digital information and at least one social index are maintained. The social index includes topics that each relate to one or more of the articles. Topic models matched to the digital information are retrieved for each topic. The articles are classified under the topics using the topic models. Each of the topics in the social index is evaluated for hotness. A plurality of time periods projected from the present is defined. Counts of the articles appearing under each time period are evaluated. The topics exhibiting a rising curve in the count of the articles that increases with recency during the time periods are chosen. Quality of the articles within the topics chosen is analyzed. The topics including the articles having acceptable quality are presented.
    • 提供了一种通过检测社会指标中的热点话题来管理用户注意力的系统和方法。 维护数字资料和至少一项社会指标。 社会指数包括与一个或多个文章相关的主题。 为每个主题检索与数字信息匹配的主题模型。 使用主题模型将文章分类为主题。 评估社会指数中的每个主题的热度。 定义从当前投影的多个时间段。 对每个时间段出现的文章进行计数。 选择在时间段内随着新近度增加的文章数量呈上升曲线的主题。 分析所选题目内的文章质量。 介绍了具有可接受质量的文章。
    • 100. 发明申请
    • System And Method For Providing Robust Topic Identification In Social Indexes
    • 在社会指标中提供可靠主题识别的系统和方法
    • US20100125540A1
    • 2010-05-20
    • US12608929
    • 2009-10-29
    • Mark J. StefikLance E. GoodSanjay Mittal
    • Mark J. StefikLance E. GoodSanjay Mittal
    • G06F15/18
    • G06F17/30
    • A computer-implemented method for providing robust topic identification in social indexes is described. Electronically-stored articles and one or more indexes are maintained. Each index includes topics that each relate to one or more of the articles. A random sampling and a selective sampling of the articles are both selected. For each topic, characteristic words included in the articles in each of the random sampling and the selective sampling are identified. Frequencies of occurrence of the characteristic words in each of the random sampling and the selective sampling are determined. A ratio of the frequencies of occurrence for the characteristic words included in the random sampling and the selective sampling is identified. Finally, for each topic, a coarse-grained topic model is built, which includes the characteristic words included in the articles relating to the topic and scores assigned to those characteristic words.
    • 描述了一种用于在社会指标中提供强大话题识别的计算机实现方法。 保存电子存储的物品和一个或多个索引。 每个索引包括与一个或多个文章相关的主题。 选择随机抽样和选择性抽样。 对于每个主题,识别包括在随机抽样和选择抽样中的每一个中的文章中的特征词。 确定随机抽样和选择抽样中每个特征词出现的频率。 识别包括在随机抽样和选择抽样中的特征词的出现频率的比率。 最后,对于每个主题,构建了一个粗粒度主题模型,其中包括与主题相关的文章中包含的特征词和分配给这些特征词的分数。