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1. 发明申请

US20150205866A1 PHRASE RESTRICTED SUBSTITUTE TERMS 审中-公开
标题翻译： PHRASE限制替代条款
公开(公告)号：US20150205866A1
公开(公告)日：2015-07-23
申请号：US13485731
申请日：2012-05-31
申请人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer , P. Pandurang Nayak
发明人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer , P. Pandurang Nayak
IPC分类号： G06F17/30
CPC分类号： G06F16/951
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for retrieving documents. One of the methods includes receiving a search query that includes a first query term and an adjacent, second query term, and a substitute term for the first query term. A determination is made that the first query term and the substitute term satisfy one or more predetermined criteria and that a resource does not include the first query term. The resource is selected to be scored only if the substitute term occurs adjacent to the second term in the resource.
摘要翻译：方法，系统和装置，包括在计算机存储介质上编码的用于检索文档的计算机程序。其中一种方法包括接收包括第一查询项和相邻第二查询项的搜索查询以及第一查询项的替代项。确定第一查询项和替代项满足一个或多个预定标准，并且资源不包括第一查询项。只有当替代项在资源中与第二项相邻时，资源才被选择进行计分。

2. 发明授权

US08631019B1 Restricted-locality synonyms 有权
公开(公告)号：US08631019B1
公开(公告)日：2014-01-14
申请号：US13372342
申请日：2012-02-13
申请人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer
发明人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer
IPC分类号： G06F17/30
CPC分类号： G06F17/30672
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying and scoring restricted-locality synonyms. In one aspect, a method includes receiving a search query including a query term and a synonym for the query term. The synonym is evaluated using one or more predetermined criteria and based on the evaluation is designated as a restricted-locality synonym. A first scoring model is selected that specifies how to score occurrences of restricted-locality synonyms in documents. A document is received that is identified as being responsive to the search query. A document score for the document is determined using the first scoring model.

3. 发明授权

US08719282B1 Restricted-locality synonyms 有权
标题翻译：受限制的同义词
公开(公告)号：US08719282B1
公开(公告)日：2014-05-06
申请号：US13403849
申请日：2012-02-23
申请人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer
发明人： Hayden Shaw , Robert B. Avery , Trystan G. Upstill , Thomas Strohmann , Pi-Chuan Chang , John Blitzer
IPC分类号： G06F17/30
CPC分类号： G06F17/30672
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying and scoring restricted-locality synonyms. In one aspect, a method includes receiving a search query including a query term and a synonym for the query term. The synonym is evaluated using one or more predetermined criteria and based on the evaluation is designated as a restricted-locality synonym. A first scoring model is selected that specifies how to score occurrences of restricted-locality synonyms in documents. A document is received that is identified as being responsive to the search query. A document score for the document is determined using the first scoring model.
摘要翻译：方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于识别和计分限制位置同义词。一方面，一种方法包括接收包括询问项和搜索项的同义词的搜索查询。使用一个或多个预定标准来评估同义词，并且基于评估被指定为限制地点同义词。选择第一个评分模型，指定如何评估文档中限制位置同义词的出现次数。接收到被识别为响应于搜索查询的文档。使用第一个评分模型确定文档的文档分数。

4. 发明授权

US08452585B2 Discriminative syntactic word order model for machine translation 有权
标题翻译：用于机器翻译的判别句法词序列模型
公开(公告)号：US08452585B2
公开(公告)日：2013-05-28
申请号：US12061313
申请日：2008-04-02
申请人： Kristina Nikolova Toutanova , Pi-Chuan Chang
发明人： Kristina Nikolova Toutanova , Pi-Chuan Chang
IPC分类号： G06F17/27
CPC分类号： G06F17/2818
摘要： A discriminatively trained word order model is used to identify a most likely word order from a set of word orders for target words translated from a source sentence. For each set of word orders, the discriminatively trained word order model uses features based on information in a source dependency tree and a target dependency tree and features based on the order of words in the word order. The discriminatively trained statistical model is trained by determining a translation metric for each of a set of N-best word orders for a set of target words. Each of the N-best word orders are projective with respect to a target dependency tree and the N-best word orders are selected using a combination of an n-gram language model and a local tree order model.
摘要翻译：使用歧视性训练的词序模型来识别从源语句翻译的目标词的一组单词顺序中最可能的单词顺序。对于每个单词组，鉴别训练的词序模型基于源依赖树中的信息和目标依赖关系树和基于单词中单词顺序的特征来使用特征。通过针对一组目标单词的一组N个最佳单词顺序中的每一个确定翻译度量来训练经歧视地训练的统计模型。每个N最好的单词顺序相对于目标依赖关系树是投影的，并且使用n-gram语言模型和本地树顺序模型的组合来选择N个最好的单词顺序。

5. 发明申请

US20080319736A1 Discriminative Syntactic Word Order Model for Machine Translation 有权
标题翻译：机器翻译判别语法词序模型
公开(公告)号：US20080319736A1
公开(公告)日：2008-12-25
申请号：US12061313
申请日：2008-04-02
申请人： Kristina Nikolova Toutanova , Pi-Chuan Chang
发明人： Kristina Nikolova Toutanova , Pi-Chuan Chang
IPC分类号： G06F17/27
CPC分类号： G06F17/2818
摘要： A discriminatively trained word order model is used to identify a most likely word order from a set of word orders for target words translated from a source sentence. For each set of word orders, the discriminatively trained word order model uses features based on information in a source dependency tree and a target dependency tree and features based on the order of words in the word order. The discriminatively trained statistical model is trained by determining a translation metric for each of a set of N-best word orders for a set of target words. Each of the N-best word orders are projective with respect to a target dependency tree and the N-best word orders are selected using a combination of an n-gram language model and a local tree order model.
摘要翻译：使用歧视性训练的词序模型来识别从源语句翻译的目标词的一组单词顺序中最可能的单词顺序。对于每个单词组，鉴别训练的词序模型基于源依赖树中的信息和目标依赖关系树和基于单词中单词顺序的特征来使用特征。通过针对一组目标单词的一组N个最佳单词顺序中的每一个确定翻译度量来训练经歧视地训练的统计模型。每个N最好的单词顺序相对于目标依赖关系树是投影的，并且使用n-gram语言模型和本地树顺序模型的组合来选择N个最好的单词顺序。

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