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    • 3. 发明申请
    • RELEVANCE SCORE IN A PAID SEARCH ADVERTISEMENT SYSTEM
    • 相关搜索广告系统中的相关分数
    • US20090327265A1
    • 2009-12-31
    • US12147417
    • 2008-06-26
    • Mingyu WangWeibin ZhuYing LiQiaolin Mao
    • Mingyu WangWeibin ZhuYing LiQiaolin Mao
    • G06F17/30
    • G06F17/30864G06Q30/02
    • Described is a paid search advertising technology in which advertisements associated with bidding keywords are ranked by relevance when returning one or more advertisements in a response to a query. A relevance score is computed for an advertisement based on the bidding keyword and page data (text and/or other page content) of the advertisement. The relevance score may be based on a similarity vector score computed from a keyword vector and page data vector relationship, combined with a proximity score computed from the keyword's bigram set and the page data bigram set. When a query is received, advertisements are selected based on the proximity of the query to each advertisement's bidding keyword, providing candidate scores. Each candidate score is modified (e.g., multiplied) into a final score based on its respective advertisement's relevance score. The final scores are then used to re-rank the advertisements relative to one another.
    • 描述了一种付费搜索广告技术,其中在与查询的响应中返回一个或多个广告时,与投标关键词相关联的广告被排列为相关性。 基于广告的出价关键字和页面数据(文本和/或其他页面内容)来计算广告的相关性分数。 相关性分数可以基于从关键字向量和页面数据向量关系计算的相似性向量分数,以及从关键字的二值组合和页面数据二进制集合计算的邻近分数。 当接收到查询时,基于查询对每个广告的出价关键词的接近度来选择广告,提供候选分数。 基于其各自的广告的相关性得分,将每个候选得分修改(例如,乘以)到最终得分中。 然后,最终得分用于相对于彼此重新排列广告。
    • 4. 发明授权
    • Relevance score in a paid search advertisement system
    • 付费搜索广告系统中的相关性得分
    • US08065311B2
    • 2011-11-22
    • US12147417
    • 2008-06-26
    • Mingyu WangWeibin ZhuYing LiQiaolin Mao
    • Mingyu WangWeibin ZhuYing LiQiaolin Mao
    • G06F7/00G06F17/30
    • G06F17/30864G06Q30/02
    • Described is a paid search advertising technology in which advertisements associated with bidding keywords are ranked by relevance when returning one or more advertisements in a response to a query. A relevance score is computed for an advertisement based on the bidding keyword and page data (text and/or other page content) of the advertisement. The relevance score may be based on a similarity vector score computed from a keyword vector and page data vector relationship, combined with a proximity score computed from the keyword's bigram set and the page data bigram set. When a query is received, advertisements are selected based on the proximity of the query to each advertisement's bidding keyword, providing candidate scores. Each candidate score is modified (e.g., multiplied) into a final score based on its respective advertisement's relevance score. The final scores are then used to re-rank the advertisements relative to one another.
    • 描述了一种付费搜索广告技术,其中在与查询的响应中返回一个或多个广告时,与投标关键词相关联的广告被排列为相关性。 基于广告的出价关键字和页面数据(文本和/或其他页面内容)来计算广告的相关性分数。 相关性分数可以基于从关键字向量和页面数据向量关系计算的相似性向量分数,以及从关键字的二值组合和页面数据二进制集合计算的邻近分数。 当接收到查询时,基于查询对每个广告的出价关键词的接近度来选择广告,提供候选分数。 基于其各自的广告的相关性得分,将每个候选得分修改(例如,乘以)到最终得分中。 然后,最终得分用于相对于彼此重新排列广告。
    • 5. 发明授权
    • Graph-based keyword expansion
    • 基于图表的关键字扩展
    • US08290975B2
    • 2012-10-16
    • US12046481
    • 2008-03-12
    • Chi GaoMingyu WangWeibin Zhu
    • Chi GaoMingyu WangWeibin Zhu
    • G06F7/00G06F17/30
    • G06F17/3064
    • A keyword may be expanded into related words, such as for use in information retrieval. The terms comprising words and/or phrases of a large number of documents (e.g., web pages) are processed into a graph data structure, in which the terms are represented as nodes and edges represent the relationships between the nodes, with weights for each edge representing the relevance of the relationship. The graph may be built by selecting each term of a document and considering the terms within a certain number of words to be associated with the selected term; for each such association the weight indicative of the relevance is increased. When the graph is accessed with a keyword, the edges from that keyword's node and their respective weights indicate which other nodes are most relevant to the keyword, thereby providing the corresponding expanded terms.
    • 关键字可以扩展成相关词,例如用于信息检索。 包括大量文档(例如,网页)的单词和/或短语的术语被处理成图形数据结构,其中术语被表示为节点,边缘表示节点之间的关系,每个边缘的权重 代表关系的相关性。 可以通过选择文档的每个术语并考虑与所选择的术语相关联的一定数量的单词中的术语来构建图; 对于每个这样的关联,指示相关性的权重增加。 当使用关键字访问图时,来自该关键字节点的边和它们各自的权重指示哪些其他节点与关键字最相关,从而提供相应的扩展术语。
    • 6. 发明申请
    • GRAPH-BASED KEYWORD EXPANSION
    • 基于图表的关键字扩展
    • US20090234832A1
    • 2009-09-17
    • US12046481
    • 2008-03-12
    • Chi GaoMingyu WangWeibin Zhu
    • Chi GaoMingyu WangWeibin Zhu
    • G06F17/30
    • G06F17/3064
    • Described is a technology by which a keyword may be expanded into related words, such as for use in information retrieval. The terms comprising words and/or phrases of a large number of documents (e.g., web pages) are processed into a graph data structure, in which the terms are represented as nodes and edges represent the relationships between the nodes, with weights for each edge representing the relevance of the relationship. The graph may be built by selecting each term of a document and considering the terms within a certain number of words to be associated with the selected term; for each such association the weight indicative of the relevance is increased. When the graph is accessed with a keyword, the edges from that keyword's node and their respective weights indicate which other nodes are most relevant to the keyword, thereby providing the corresponding expanded terms.
    • 描述了可以将关键字扩展成相关词的技术,例如用于信息检索。 包括大量文档(例如,网页)的单词和/或短语的术语被处理成图形数据结构,其中术语被表示为节点,边缘表示节点之间的关系,每个边缘的权重 代表关系的相关性。 可以通过选择文档的每个术语并考虑与所选择的术语相关联的一定数量的单词中的术语来构建图; 对于每个这样的关联,指示相关性的权重增加。 当使用关键字访问图时,来自该关键字节点的边和它们各自的权重指示哪些其他节点与关键字最相关,从而提供相应的扩展术语。
    • 7. 发明授权
    • Method and system for generating synthesized speech based on human recording
    • 基于人类记录生成合成语音的方法和系统
    • US07899672B2
    • 2011-03-01
    • US11475820
    • 2006-06-27
    • Yong QinLiqin ShenWei ZhangWeibin Zhu
    • Yong QinLiqin ShenWei ZhangWeibin Zhu
    • G10L13/08G10L13/00
    • G10L13/04
    • A method and system that incorporates human recording with a TTS system to generate synthesized speech with high quality by searching over a database of pre-recorded utterances to select an utterance best matching text content to be synthesized into speech; dividing the best-matched utterance into a plurality of segments to generate remaining segments that are the same as corresponding parts of the text content and difference segments that are different from corresponding parts of the text content; synthesizing speech for the parts of the text content corresponding to the difference segments; and splicing the synthesized speech segments with the remaining segments of the best-matched utterance.
    • 一种将人类记录与TTS系统相结合的方法和系统,通过在数据库上搜索预先录制的话语来选择要合成语音的最佳匹配文本内容,从而产生高质量的合成语音; 将最佳匹配的话语划分成多个段以产生与文本内容的对应部分和与文本内容的对应部分不同的差异段的剩余段; 对与差分片段相对应的文本内容的部分合成语音; 以及将合成的语音片段与最佳匹配的话语的剩余片段拼接。