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    • 3. 发明授权
    • Efficient lexical trending topic detection over streams of data using a modified sequitur algorithm
    • 使用修改的Sequitur算法对数据流进行有效的词汇趋势主题检测
    • US08838599B2
    • 2014-09-16
    • US12780850
    • 2010-05-14
    • Zhichen XuYun FuNeal Sample
    • Zhichen XuYun FuNeal Sample
    • G06F17/30
    • G06F17/30616
    • Embodiments are directed towards a Modified Sequitur algorithm (MSA) using pipelining and indexed arrays to identify trending topics within a plurality of documents having user generated content (UGC). The documents are parallelized and distributed across a plurality of network devices, which place at least some of the received documents into a buffer for which the MSA may then be applied to the documents within the buffer to identify n-grams or phrases within the documents' contents. The identified phrases are further analyzed to remove extraneous co-occurrences of phrases, and/or words based on a part of speech analysis. A weighting of the remaining phrases is used to identify trending topic phrases. Links to content in the plurality of UGC documents that is associated with the trending topic phrases may then be displayed to a client device.
    • 实施例针对使用流水线和索引数组来修改具有用户生成内容(UGC)的多个文档内的趋势主题的修改的序列算法(MSA)。 这些文档被并行化并且分布在多个网络设备上,这些网络设备将至少一些接收到的文档放置在缓冲器中,然后可以将MSA应用于缓冲器中的文档,以识别文档中的n个或多个短语, 内容。 进一步分析识别的短语,以消除基于词性分析的短语和/或单词的无关共存。 使用剩余短语的加权来识别趋势主题短语。 然后可以将与趋势主题短语相关联的多个UGC文档中的内容的链接显示给客户端设备。