专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US08515950B2 Combining log-based rankers and document-based rankers for searching 有权
标题翻译：结合基于日志的排名和基于文档的排名搜索
公开(公告)号：US08515950B2
公开(公告)日：2013-08-20
申请号：US12243678
申请日：2008-10-01
申请人： Jianfeng Gao , Kuansan Wang
发明人： Jianfeng Gao , Kuansan Wang
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30867
摘要： Log-based rankers and document-based rankers may be combined for searching. In an example embodiment, there is a method for combining rankers to perform a search operation. A count of query instances in log data is ascertained based on a query. A search for the query is performed to produce a set of search results. The set of search results is ranked by relevance score with a document-based ranker and a log-based ranker using a weighting factor that is adapted responsive to the count of the query instances in the log data.
摘要翻译：基于日志的排名者和基于文档的排名者可以组合进行搜索。在示例性实施例中，存在用于组合等级以执行搜索操作的方法。基于查询确定日志数据中的查询实例的计数。执行查询的搜索以产生一组搜索结果。搜索结果的集合通过使用基于文档的游客和基于日志的游戏者的相关性得分进行排序，该游戏者使用响应于日志数据中的查询实例的计数的加权因子。

2. 发明申请

US20100082510A1 TRAINING A SEARCH RESULT RANKER WITH AUTOMATICALLY-GENERATED SAMPLES 有权
标题翻译：用自动生成样本培养搜索结果排名
公开(公告)号：US20100082510A1
公开(公告)日：2010-04-01
申请号：US12243359
申请日：2008-10-01
申请人： Jianfeng Gao , Kuansan Wang
发明人： Jianfeng Gao , Kuansan Wang
IPC分类号： G06F15/18 , G06F7/06 , G06F17/30
CPC分类号： G06N99/005 , G06F17/3053
摘要： A search result ranker may be trained with automatically-generated samples. In an example embodiment, user interests are inferred from user interactions with search results for a particular query so as to determine respective relevance scores associated with respective query-identifier pairs of the search results. Query-identifier-relevance score triplets are formulated from the respective relevance scores associated with the respective query-identifier pairs. The query-identifier-relevance score triplets are submitted as training samples to a search result ranker. The search result ranker is trained as a learning machine with multiple training samples of the query-identifier-relevance score triplets.
摘要翻译：搜索结果筛选器可以用自动生成的样本进行训练。在一个示例性实施例中，用户兴趣从用户与特定查询的搜索结果的交互推断，以便确定与搜索结果的相应查询 - 标识符对相关联的相应关联度得分。查询标识符 - 相关性分数三元组由与相应查询 - 标识符对相关联的各个相关性得分制定。查询标识符 - 相关性分数三元组作为训练样本提交给搜索结果筛选器。搜索结果筛选器被训练为具有查询标识符相关性分数三元组的多个训练样本的学习机器。

3. 发明授权

US08060456B2 Training a search result ranker with automatically-generated samples 有权
标题翻译：用自动生成的样本训练搜索结果
公开(公告)号：US08060456B2
公开(公告)日：2011-11-15
申请号：US12243359
申请日：2008-10-01
申请人： Jianfeng Gao , Kuansan Wang
发明人： Jianfeng Gao , Kuansan Wang
IPC分类号： G06F15/16
CPC分类号： G06N99/005 , G06F17/3053
摘要： A search result ranker may be trained with automatically-generated samples. In an example embodiment, user interests are inferred from user interactions with search results for a particular query so as to determine respective relevance scores associated with respective query-identifier pairs of the search results. Query-identifier-relevance score triplets are formulated from the respective relevance scores associated with the respective query-identifier pairs. The query-identifier-relevance score triplets are submitted as training samples to a search result ranker. The search result ranker is trained as a learning machine with multiple training samples of the query-identifier-relevance score triplets.
摘要翻译：搜索结果筛选器可以用自动生成的样本进行训练。在一个示例性实施例中，用户兴趣从用户与特定查询的搜索结果的交互推断，以便确定与搜索结果的相应查询 - 标识符对相关联的相应关联度得分。查询标识符 - 相关性分数三元组由与相应查询 - 标识符对相关联的各个相关性得分制定。查询标识符 - 相关性分数三元组作为训练样本提交给搜索结果筛选器。搜索结果筛选器被训练为具有查询标识符相关性分数三元组的多个训练样本的学习机器。

4. 发明申请

US20100082582A1 COMBINING LOG-BASED RANKERS AND DOCUMENT-BASED RANKERS FOR SEARCHING 有权
标题翻译：组合基于日志的排名和基于文档的排名者进行搜索
公开(公告)号：US20100082582A1
公开(公告)日：2010-04-01
申请号：US12243678
申请日：2008-10-01
申请人： Jianfeng Gao , Kuansan Wang
发明人： Jianfeng Gao , Kuansan Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： Log-based rankers and document-based rankers may be combined for searching. In an example embodiment, there is a method for combining rankers to perform a search operation. A count of query instances in log data is ascertained based on a query. A search for the query is performed to produce a set of search results. The set of search results is ranked by relevance score with a document-based ranker and a log-based ranker using a weighting factor that is adapted responsive to the count of the query instances in the log data.
摘要翻译：基于日志的排名者和基于文档的排名者可以组合进行搜索。在示例性实施例中，存在用于组合等级以执行搜索操作的方法。基于查询确定日志数据中的查询实例的计数。执行查询的搜索以产生一组搜索结果。搜索结果的集合通过使用基于文档的游客和基于日志的游戏者的相关性得分进行排序，该游戏者使用响应于日志数据中的查询实例的计数的加权因子。

5. 发明授权

US08645289B2 Structured cross-lingual relevance feedback for enhancing search results 有权
标题翻译：结构化的跨语言相关性反馈，以增强搜索结果
公开(公告)号：US08645289B2
公开(公告)日：2014-02-04
申请号：US12970879
申请日：2010-12-16
申请人： Paul Nathan Bennett , Jianfeng Gao , Jagadeesh Jagarlamudi , Kristen Patricia Parton
发明人： Paul Nathan Bennett , Jianfeng Gao , Jagadeesh Jagarlamudi , Kristen Patricia Parton
IPC分类号： G06F15/18
CPC分类号： G06F17/30669 , G06F17/30675
摘要： A “Cross-Lingual Unified Relevance Model” provides a feedback model that improves a machine-learned ranker for a language with few training resources, using feedback from a more complete ranker for a language that has more training resources. The model focuses on linguistically non-local queries, such as “world cup” (English language/U.S. market) and “copa mundial” (Spanish language/Mexican market), that have similar user intent in different languages and markets or regions, thus allowing the low-resource ranker to receive direct relevance feedback from the high-resource ranker. Among other things, the Cross-Lingual Unified Relevance Model differs from conventional relevancy-based techniques by incorporating both query- and document-level features. More specifically, the Cross-Lingual Unified Relevance Model generalizes existing cross-lingual feedback models, incorporating both query expansion and document re-ranking to further amplify the signal from the high-resource ranker to enable a learning to rank approach based on appropriately labeled training data.
摘要翻译： “跨语言统一相关性模型”提供了一种反馈模型，可以为少数培训资源的语言改进机器学习游戏者，使用更完整的游戏者的反馈来获得具有更多培训资源的语言。该模式侧重于语言上的非本地查询，例如“世界杯”（英语/美国市场）和“复合世界”（西班牙语/墨西哥市场），在不同语言和市场或区域具有类似的用户意图，因此允许低资源游击队员接收来自高资源队员的直接相关反馈。其中，跨语言统一相关性模型与传统的相关性技术不同，包括查询和文档级功能。更具体地说，跨语言统一相关性模型概括了现有的跨语言反馈模型，其中包括查询扩展和文档重新排序，以进一步放大来自高资源游戏者的信号，以使学习能够基于适当标记的训练进行排名数据。

6. 发明申请

US20120254218A1 Enhanced Query Rewriting Through Statistical Machine Translation 有权
标题翻译：通过统计机器翻译增强查询重写
公开(公告)号：US20120254218A1
公开(公告)日：2012-10-04
申请号：US13078648
申请日：2011-04-01
申请人： Alnur Ali , Jianfeng Gao , Xiaodong He , Bodo von Billerbeck , Sanaz Ahari
发明人： Alnur Ali , Jianfeng Gao , Xiaodong He , Bodo von Billerbeck , Sanaz Ahari
IPC分类号： G06F17/30
CPC分类号： G06F17/30672
摘要： Systems, methods, and computer media for identifying query rewriting replacement terms are provided. A list of related string pairs each comprising a first string and second string is received. The first string of each related string pair is a user search query extracted from user click log data. For one or more of the related string pairs, the string pair is provided as inputs to a statistical machine translation model. The model identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string. The model also calculates a probability of relatedness for each of the one or more pairs of corresponding terms. Term pairs whose calculated probability of relatedness exceeds a threshold are characterized as query term replacements and incorporated, along with the probability of relatedness, into a query rewriting candidate database.
摘要翻译：提供了用于识别查询重写替换术语的系统，方法和计算机媒体。接收包括第一串和第二串的相关字符串对的列表。每个相关字符串对的第一个字符串是从用户点击日志数据中提取的用户搜索查询。对于一个或多个相关字符串对，字符串对作为统计机器翻译模型的输入提供。该模型识别一对或多对对应的术语，每对对应的术语包括来自第一个字符串的第一项和来自第二个字符串的第二个项。该模型还计算一对或多对相应项中的每一对的相关概率。其相关性概率超过阈值的术语对被表征为查询词替换，并将其与相关性的概率一起并入查询重写候选数据库中。

7. 发明申请

US20120131031A1 DEPENDENCY-BASED QUERY EXPANSION ALTERATION CANDIDATE SCORING 有权
标题翻译：基于依赖性的查询扩展替换候选评分
公开(公告)号：US20120131031A1
公开(公告)日：2012-05-24
申请号：US12951068
申请日：2010-11-22
申请人： Shasha Xie , Xiaodong He , Jianfeng Gao
发明人： Shasha Xie , Xiaodong He , Jianfeng Gao
IPC分类号： G06F17/30
CPC分类号： G06F17/30967 , G06F17/30672
摘要： An alteration candidate for a query can be scored. The scoring may include computing one or more query-dependent feature scores and/or one or more intra-candidate dependent feature scores. The computation of the query-dependent feature score(s) can be based on dependencies to multiple query terms from each of one or more alteration terms (i.e., for each of the one or more alteration terms, there can be dependencies to multiple query terms that form at least a portion of the basis for the query-dependent feature score(s)). The computation of the intra-candidate dependent feature score(s) can be based on dependencies between different terms in the alteration candidate. A candidate score can be computed using the query dependent feature score(s) and/or the intra-candidate dependent feature score(s). Additionally, the candidate score can be used in determining whether to select the candidate to expand the query. If selected, the candidate can be used to expand the query.
摘要翻译：可以对查询的变更候选进行评分。评分可以包括计算一个或多个依赖于查询的特征得分和/或一个或多个候选内相关特征得分。依赖于查询的特征得分的计算可以基于来自一个或多个改变项中的每一个的多个查询词的依赖性（即，对于一个或多个改变术语中的每一个，可以依赖于多个查询术语其形成用于查询相关特征得分的基础的至少一部分）。候选者相关特征得分的计算可以基于变更候选者中不同术语之间的依赖关系。可以使用查询相关特征得分和/或候选内相关特征得分来计算候选分数。此外，可以使用候选分数来确定是否选择候选来扩展查询。如果选择，候选人可以用来扩展查询。

8. 发明授权

US08060358B2 HMM alignment for combining translation systems 有权
标题翻译：用于组合翻译系统的HMM对齐
公开(公告)号：US08060358B2
公开(公告)日：2011-11-15
申请号：US12147807
申请日：2008-06-27
申请人： Xiaodong He , Mei Yang , Jianfeng Gao , Patrick Nguyen
发明人： Xiaodong He , Mei Yang , Jianfeng Gao , Patrick Nguyen
IPC分类号： G06F17/28
CPC分类号： G06F17/2827 , G06F17/2818
摘要： A computing system configured to produce an optimized translation hypothesis of text input into the computing system. The computing system includes a plurality of translation machines. Each of the translation machines is configured to produce their own translation hypothesis from the same text. An optimization machine is connected to the plurality of translation machines. The optimization machine is configured to receive the translation hypotheses from the translation machines. The optimization machine is further configured to align, word-to-word, the hypotheses in the plurality of hypotheses by using a hidden Markov model.
摘要翻译：一种计算系统，被配置为产生文本输入到所述计算系统中的优化翻译假说。计算系统包括多个翻译机。每个翻译机被配置为从相同的文本产生他们自己的翻译假设。优化机连接到多台翻译机。优化机被配置为从翻译机接收翻译假说。优化机还被配置为通过使用隐马尔科夫模型来对齐单词到多个假设中的假设。

9. 发明授权

US07974963B2 Method and system for retrieving confirming sentences 有权
标题翻译：检索确认句子的方法和系统
公开(公告)号：US07974963B2
公开(公告)日：2011-07-05
申请号：US11187567
申请日：2005-07-22
申请人： Ming Zhou , Hua Wu , Yue Zhang , Jianfeng Gao , Chang-Ning Huang
发明人： Ming Zhou , Hua Wu , Yue Zhang , Jianfeng Gao , Chang-Ning Huang
IPC分类号： G06F17/00
CPC分类号： G06F17/3069 , Y10S707/99933
摘要： A method, computer readable medium and system are provided which retrieve confirming sentences from a sentence database in response to a query. A search engine retrieves confirming sentences from the sentence database in response to the query. IN retrieving the confirming sentences, the search engine defines indexing units based upon the query, with the indexing units including both lemma from the query and extended indexing units associated with the query. The search engine then retrieves a plurality of sentences from the sentence database using the defined indexing units as search parameters. A similarity between each of the plurality of retrieved sentences and the query is determined by the search engine, wherein each similarity is determined as a function of a linguistic weight of a term in the query. The search engine then ranks the plurality of retrieved sentences based upon the determined similarities.
摘要翻译：提供了一种方法，计算机可读介质和系统，其响应于查询从句子数据库中检索确认句子。搜索引擎响应于查询从句子数据库中检索确认句子。在检索确认语句中，搜索引擎基于查询来定义索引单元，索引单元包括来自查询的引理和与查询相关联的扩展索引单元。然后，搜索引擎使用定义的索引单元作为搜索参数从句子数据库中检索多个句子。由搜索引擎确定多个检索到的句子和查询中的每一个之间的相似度，其中每个相似度被确定为查询中的术语的语言权重的函数。然后，搜索引擎基于所确定的相似度对多个检索到的句子进行排序。

10. 发明申请

US20100153315A1 BOOSTING ALGORITHM FOR RANKING MODEL ADAPTATION 有权
标题翻译：用于排序模型适应的增强算法
公开(公告)号：US20100153315A1
公开(公告)日：2010-06-17
申请号：US12337623
申请日：2008-12-17
申请人： Jianfeng Gao , Yi Su , Qiang Wu , Chris J.C. Burges , Krysta Svore , Elbio Renato Torres Abib
发明人： Jianfeng Gao , Yi Su , Qiang Wu , Chris J.C. Burges , Krysta Svore , Elbio Renato Torres Abib
IPC分类号： G06F15/18 , G06F17/30
CPC分类号： G06F17/3053
摘要： Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted.
摘要翻译：可以执行模型适配以采用用一组训练数据（可能较大）训练的通用模型，并且使用一组特定领域的训练数据（可能小）来适配模型。在一个域（称为背景域）中训练的模型的参数，结构或配置可以适应于可能存在有限量的训练数据的不同域（称为适配域）。可以使用升压算法来执行自适应，以选择最优基函数，该优化基函数优化模型的误差量度，因为其被迭代地改进，即适应。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式