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公开(公告)号：US08768050B2
公开(公告)日：2014-07-01
申请号：US13158484
申请日：2011-06-13
IPC分类号： G06K9/62
CPC分类号： G06K9/6268 , G06K9/6227 , G06K9/6293
摘要： Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest.
摘要翻译： 产品图像与文本描述结合使用，以改进产品分类。 通过结合来自与产品相关的文本和图像描述的提示，实现在基于网络的商业搜索的上下文中增强了产品描述分类的精度和回收。 几个实现旨在改进那些仅文本方法最不可靠的领域。 例如，在简短的文本产品描述使用与多个不同类别重叠的词汇的情况下，多个实现使用图像信号来补充文本分类器并改进整体产品分类。 其他实现涉及使用文本和图像“训练集”来改进自动分类器，包括纯文本分类器。 某些实现也针对学习一些三维图像分类器，仅针对文本信号的“混淆类别”，以改善文本分类最弱的特定领域。
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公开(公告)号：US20120314941A1
公开(公告)日：2012-12-13
申请号：US13158484
申请日：2011-06-13
IPC分类号： G06K9/62
CPC分类号： G06K9/6268 , G06K9/6227 , G06K9/6293
摘要： Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest.
摘要翻译： 产品图像与文本描述结合使用，以改进产品分类。 通过结合来自与产品相关的文本和图像描述的提示，实现在基于网络的商业搜索的上下文中增强了产品描述分类的精度和回收。 几个实现旨在改进那些仅文本方法最不可靠的领域。 例如，在简短的文本产品描述使用与多个不同类别重叠的词汇的情况下，多个实现使用图像信号来补充文本分类器并改进整体产品分类。 其他实现涉及使用文本和图像训练集来改进自动分类器，包括纯文本分类器。 某些实现也针对学习一些三维图像分类器，仅针对混淆文本信号的类别，以改进文本分类最弱的特定区域。
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公开(公告)号：US10275525B2
公开(公告)日：2019-04-30
申请号：US13517603
申请日：2012-06-14
申请人： Vidit Jain , Nikhil Rasiwasia
发明人： Vidit Jain , Nikhil Rasiwasia
IPC分类号： G06F17/30
摘要： A method and system for mining trends around trending terms. The method includes determining a plurality of articles, from one or more websites, in relation to a first entity for a time period. The first entity is a trending term. The method also includes generating comment clusters for the plurality of articles. Each comment cluster is generated for associated article and includes plurality of user comments. The method further includes extracting one or more entities from plurality of user comments for each of the comment clusters, the one or more entities related to the first entity. Further, the method includes enabling selection of a second entity, from the one or more entities, by the user. Moreover, the method includes rendering one or more user comments corresponding to the first entity and the second entity for the time period. The system includes an electronic device, communication interface, memory, and processor.
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