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公开(公告)号：US09852430B2
公开(公告)日：2017-12-26
申请号：US11538309
申请日：2006-10-03
申请人： Ming Zhou , Cheng Niu , Zhaohui Tang , Ying Li , Chin-Yew Lin , Li Li , Brian Burdick
发明人： Ming Zhou , Cheng Niu , Zhaohui Tang , Ying Li , Chin-Yew Lin , Li Li , Brian Burdick
CPC分类号： G06Q30/02 , G06Q30/0242 , G06Q30/0255 , G06Q30/0261 , G06Q30/0269 , G06Q30/0276
摘要： Systems, methods, and computer-readable media for dynamically generating text associated with an advertisement are provided. Core text associated with an advertisement is received from an advertiser, as is at least one attribute relevant to the advertiser and/or a user. Based upon the received attribute(s), it is determined whether customization of the core text is desired. If customization is desired, the core text is modified and presented in association with the advertisement. If customization is not desired, the core text is presented in association with the advertisement. In one embodiment, target advertisement placement information may also be utilized to determine whether customization of the core text is desired.
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公开(公告)号：US20080082410A1
公开(公告)日：2008-04-03
申请号：US11538309
申请日：2006-10-03
申请人： Ming Zhou , Cheng Niu , Zhaohui Tang , Ying Li , Chin-Yew Lin , Li Li , Brian Burdick
发明人： Ming Zhou , Cheng Niu , Zhaohui Tang , Ying Li , Chin-Yew Lin , Li Li , Brian Burdick
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0242 , G06Q30/0255 , G06Q30/0261 , G06Q30/0269 , G06Q30/0276
摘要： Systems, methods, and computer-readable media for dynamically generating text associated with an advertisement are provided. Core text associated with an advertisement is received from an advertiser, as is at least one attribute relevant to the advertiser and/or a user. Based upon the received attribute(s), it is determined whether customization of the core text is desired. If customization is desired, the core text is modified and presented in association with the advertisement. If customization is not desired, the core text is presented in association with the advertisement. In one embodiment, target advertisement placement information may also be utilized to determine whether customization of the core text is desired.
摘要翻译： 提供了用于动态生成与广告相关联的文本的系统，方法和计算机可读介质。 从广告商接收与广告相关联的核心文本，以及与广告主和/或用户相关的至少一个属性。 基于接收到的属性，确定是否需要定制核心文本。 如果需要定制，核心文本将与广告相关联地进行修改和呈现。 如果不希望进行定制，则与广告相关联地呈现核心文本。 在一个实施例中，还可以利用目标广告布置信息来确定是否需要定制核心文本。
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公开(公告)号：US20100082511A1
公开(公告)日：2010-04-01
申请号：US12241078
申请日：2008-09-30
申请人： Cheng Niu , Ming Zhou , Hsiao-Wuen Hon
发明人： Cheng Niu , Ming Zhou , Hsiao-Wuen Hon
CPC分类号： G06F17/30675
摘要： Described is a technology in which a classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
摘要翻译： 描述了一种技术，其中构建分类器以至少部分地基于与其他文档的相似性以及这些其他文档与查询的相关性来对在查询中发现的不同语言的文档进行排名。 联合排名模型，例如基于玻尔兹曼（Boltzmann）机器，用于表示文档之间的内容相似性，并且帮助确定一组文档的联合相关概率。 因此，利用一种语言的相关文件来改进不同语言文件的相关性估计。 在一个方面，隐藏的单位（神经元）表示检索的文档中的集群（对应于相关主题），输出层表示相关文档及其特征，边缘表示集群和文档之间的关系。
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公开(公告)号：US08051061B2
公开(公告)日：2011-11-01
申请号：US12033308
申请日：2008-02-19
IPC分类号： G06F17/30
CPC分类号： G06F17/30669 , G06F17/30672
摘要： Cross-lingual query suggestion (CLQS) aims to suggest relevant queries in a target language for a given query in a source language. The cross-lingual query suggestion is improved by exploiting the query logs in the target language. CLQS provides a method for learning and determining a similarity measure between two queries in different languages. The similarity measure is based on both translation information and monolingual similarity information, and in one embodiment uses both the query log itself and click-through information associated therewith. Monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics may be used to estimate the cross-lingual query similarity with a discriminative model.
摘要翻译： 跨语言查询建议（CLQS）旨在以源语言为给定查询以目标语言建议相关查询。 通过利用目标语言的查询日志来改进跨语言查询建议。 CLQS提供了一种用于学习和确定不同语言的两个查询之间的相似性度量的方法。 相似性度量基于翻译信息和单语相似性信息，并且在一个实施例中使用查询日志本身和与之相关联的点击信息。 可以使用单词和跨语言信息（例如单词翻译关系和单词同现统计）来用歧视模型估计跨语言查询相似性。
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公开(公告)号：US20090182547A1
公开(公告)日：2009-07-16
申请号：US12015491
申请日：2008-01-16
IPC分类号： G06F17/28
CPC分类号： G06F17/2827
摘要： Mining of translation pairs for cross-language translation uses a collective extraction model to exploit the similarity among the translation pairs and adaptively learn extraction patterns for each bilingual webpage. The process queries a web search engine by an initial term translation list to retrieve bilingual webpages containing translations, and crawls websites hosting the retreived bilingual webpages to retrieve additional bilingual webpages. The process then extracts additional translation pairs from the bilingual webpages retrieved by learning translation patterns of the bilingual webpages retrieved and adaptively extreacting translation pairs from the bilingual webpages using the learned translation patterns. More bilingual webpages may be acquired for additional website crawling and translation pair extracting by querying the web search engine by additional translation pairs.
摘要翻译： 跨语言翻译的翻译对的挖掘使用集体提取模型来利用翻译对之间的相似性，并自适应地学习每个双语网页的提取模式。 该过程通过初始术语翻译列表查询网页搜索引擎，以检索包含翻译的双语网页，并抓取托管已回收的双语网页的网站以检索其他双语网页。 然后，该过程从通过学习所检索的双语网页的翻译模式检索的双语网页中提取另外的翻译对，并使用所学习的翻译模式从双语网页中自适应地排除翻译对。 可以通过额外的翻译对查询网页搜索引擎来获取更多的双语网页，用于额外的网站抓取和翻译对提取。
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6.发明申请公开(公告)号：US20080010056A1
公开(公告)日：2008-01-10
申请号：US11483941
申请日：2006-07-10
IPC分类号： G06F17/20
CPC分类号： G06F17/2827 , G06F17/30014 , G06F17/30882
摘要： A set of candidate parallel pages is identified based on trigger words in one or more pages downloaded from a given network location (such as a website). A set of document trees representing each of the candidate pages are aligned to identify translationally parallel content and hyperlinks. The parallel content is further fed into conventional sentence aligner for parallel sentences. And the parallel hyperlinks usually refer to other parallel documents, and lead to a recursive mining of parallel documents.
摘要翻译： 基于从给定网络位置（诸如网站）下载的一个或多个页面中的触发词来识别一组候选并行页面。 表示每个候选页面的一组文档树被对齐以识别平移的平行内容和超链接。 将并行内容进一步馈送到常规句子对齐器中用于并行句子。 并行超链接通常是指其他并行文档，并导致并行文档的递归挖掘。
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公开(公告)号：US08249855B2
公开(公告)日：2012-08-21
申请号：US11500051
申请日：2006-08-07
IPC分类号： G06F17/28
CPC分类号： G06F17/2827 , G06F17/3066 , G06F17/30864
摘要： A set of candidate documents, each of which may be part of a bilingual, parallel set of documents, are identified. The set of documents illustratively includes textual material in a source language. It is then determined whether parallel text can be identified. For each document in the set of documents, it is first determined whether the parallel text resides within the document itself. If not, the document is examined for links to other documents, and those linked documents are examined for bilingual parallelism with the selected documents. If not, named entities are extracted from the document and translated into the target language. The translations are used to query search engines to retrieve the parallel correspondent for the selected documents.
摘要翻译： 确定了一组候选文件，每个候选文件可能是双语平行文件的一部分。 该组文件说明性地包含源语言的文本材料。 然后确定是否可以识别并行文本。 对于文档集中的每个文档，首先确定并行文本是否位于文档本身内。 如果没有，该文件将被检查到其他文件的链接，并且这些链接的文档被检查与选择的文档的双语并行性。 如果没有，则从文档中提取命名实体并将其翻译成目标语言。 翻译用于查询搜索引擎以检索所选文档的并行通讯者。
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公开(公告)号：US07917488B2
公开(公告)日：2011-03-29
申请号：US12041629
申请日：2008-03-03
CPC分类号： G06F17/30864
摘要： Cross-lingual search re-ranking is performed during a cross-lingual search in which a search query of a first language is used to retrieve two sets of documents, a first set in the first language, and a second set in a second language. The two sets of documents are each first ranked by the search engine separately. Cross-lingual search re-ranking then aims to provide a uniform re-ranking of both sets of documents combined. Cross-lingual search re-ranking uses a unified ranking function to compute the ranking order of each document of the first set and the second set of documents. The unified ranking function is constructed using generative probabilities based on multiple features, and can be learned by optimizing weight parameters using a training corpus. Ranking SVM algorithms may be used for the optimization.
摘要翻译： 在跨语言搜索期间执行跨语言搜索重新排序，其中使用第一语言的搜索查询来检索两组文档，第一语言中的第一集合和第二语言的第二集合。 两组文件分别由搜索引擎排列。 跨语言搜索重新排序的目的是提供两套文件的统一重新排列。 跨语言搜索重新排序使用统一排序函数来计算第一组和第二组文档的每个文档的排序顺序。 统一排序函数是利用基于多个特征的生成概率构建的，可以通过使用训练语料库优化权重参数来获得。 排序SVM算法可用于优化。
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公开(公告)号：US08326785B2
公开(公告)日：2012-12-04
申请号：US12241078
申请日：2008-09-30
申请人： Cheng Niu , Ming Zhou , Hsiao-Wuen Hon
发明人： Cheng Niu , Ming Zhou , Hsiao-Wuen Hon
CPC分类号： G06F17/30675
摘要： A classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
摘要翻译： 构建分类器至少部分地基于与其他文档的相似性以及这些其他文档与查询的相关性来对查询中发现的不同语言的文档进行排序。 联合排名模型，例如基于玻尔兹曼（Boltzmann）机器，用于表示文档之间的内容相似性，并且帮助确定一组文档的联合相关概率。 因此，利用一种语言的相关文件来改进不同语言文件的相关性估计。 在一个方面，隐藏的单位（神经元）表示检索的文档中的集群（对应于相关主题），输出层表示相关文档及其特征，边缘表示集群和文档之间的关系。
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公开(公告)号：US20090222437A1
公开(公告)日：2009-09-03
申请号：US12041629
申请日：2008-03-03
CPC分类号： G06F17/30864
摘要： Cross-lingual search re-ranking is performed during a cross-lingual search in which a search query of a first language is used to retrieve two sets of documents, a first set in the first language, and a second set in a second language. The two sets of documents are each first ranked by the search engine separately. Cross-lingual search re-ranking then aims to provide a uniform re-ranking of both sets of documents combined. Cross-lingual search re-ranking uses a unified ranking function to compute the ranking order of each document of the first set and the second set of documents. The unified ranking function is constructed using generative probabilities based on multiple features, and can be learned by optimizing weight parameters using a training corpus. Ranking SVM algorithms may be used for the optimization.
摘要翻译： 在跨语言搜索期间执行跨语言搜索重新排序，其中使用第一语言的搜索查询来检索两组文档，第一语言中的第一集合和第二语言的第二集合。 这两组文件分别由搜索引擎排列。 跨语言搜索重新排序的目的是提供两套文件的统一重新排列。 跨语言搜索重新排序使用统一排序函数来计算第一组和第二组文档的每个文档的排序顺序。 统一排序函数是利用基于多个特征的生成概率构建的，可以通过使用训练语料库优化权重参数来获得。 排序SVM算法可用于优化。
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