公开(公告)号：US20120096042A1

公开(公告)日：2012-04-19

申请号：US12907031

申请日：2010-10-19

申请人： Christopher John Brockett ,  Jianfeng Gao ,  Vahed Qazvinian

发明人： Christopher John Brockett ,  Jianfeng Gao ,  Vahed Qazvinian

IPC分类号： G06F17/30

CPC分类号： G06F17/30448

摘要： There is provided a computer-implemented method for user query reformulation. A graph is created to represent a relationship between previous user query terms. The graph may represent the previous user searches in n-grams that correspond to nodes. A random walk analysis is performed to determine probabilities that various n-grams corresponding to nodes of the graph could be used to effectively alter a user search term. The probabilities represent a quantification of relationships between nodes of the graph. A determination may be made regarding whether to reformulate the user query based on a relationship between a user search term in the user query and a graphed search term represented by a node of the graph. The determination takes into account a relationship between the user search term and the graphed search term.

摘要翻译： 提供了一种用于用户查询重新设计的计算机实现的方法。 创建一个图表来表示先前的用户查询词之间的关系。 该图可以代表与n个节点对应的先前用户搜索。 执行随机游走分析以确定可以使用对应于图的节点的各种n-gram来有效地改变用户搜索项的概率。 概率表示图的节点之间的关系的量化。 可以基于用户查询中的用户搜索项和由图的节点表示的图形搜索项之间的关系来确定是否重新形成用户查询。 该确定考虑了用户搜索项和图形搜索项之间的关系。

公开(公告)号：US09092483B2

公开(公告)日：2015-07-28

申请号：US12907031

申请日：2010-10-19

申请人： Christopher John Brockett ,  Jianfeng Gao ,  Vahed Qazvinian

发明人： Christopher John Brockett ,  Jianfeng Gao ,  Vahed Qazvinian

IPC分类号： G06F17/30

CPC分类号： G06F17/30448

摘要： There is provided a computer-implemented method for user query reformulation. A graph is created to represent a relationship between previous user query terms. The graph may represent the previous user searches in n-grams that correspond to nodes. A random walk analysis is performed to determine probabilities that various n-grams corresponding to nodes of the graph could be used to effectively alter a user search term. The probabilities represent a quantification of relationships between nodes of the graph. A determination may be made regarding whether to reformulate the user query based on a relationship between a user search term in the user query and a graphed search term represented by a node of the graph. The determination takes into account a relationship between the user search term and the graphed search term.

摘要翻译： 提供了一种用于用户查询重新设计的计算机实现的方法。 创建一个图表来表示先前的用户查询词之间的关系。 该图可以代表与n个节点对应的先前用户搜索。 执行随机游走分析以确定可以使用对应于图的节点的各种n-gram来有效地改变用户搜索项的概率。 概率表示图的节点之间的关系的量化。 可以基于用户查询中的用户搜索项和由图的节点表示的图形搜索项之间的关系来确定是否重新形成用户查询。 该确定考虑了用户搜索项和图形搜索项之间的关系。

公开(公告)号：US09501759B2

公开(公告)日：2016-11-22

申请号：US13328924

申请日：2011-12-16

申请人： Jianfeng Gao ,  Xuedong Huang ,  Mei Li ,  Zhenghao Wang ,  Christopher John Brockett ,  William B. Dolan

发明人： Jianfeng Gao ,  Xuedong Huang ,  Mei Li ,  Zhenghao Wang ,  Christopher John Brockett ,  William B. Dolan

IPC分类号： G06F17/30 ,  G06F7/00 ,  G06Q10/10 ,  G06Q30/02

CPC分类号： G06Q10/10 ,  G06F17/30687 ,  G06Q30/0241

摘要： The subject disclosure is directed towards developing a translation model for mapping search query terms to document-related data. By processing user logs comprising search histories into word-aligned query-document pairs, the translation model may be trained using data, such as probabilities, corresponding to the word-aligned query-document pairs. After incorporating the translation model into model data for a search engine, the translation model is used may used as features for producing relevance scores for current search queries and ranking documents/advertisements according to relevance.

摘要翻译： 本发明旨在开发用于将搜索查询词语映射到文档相关数据的翻译模型。 通过将包括搜索历史的用户日志处理成字对齐的查询 - 文档对，可以使用对应于字对齐的查询 - 文档对的诸如概率的数据来训练翻译模型。 在将翻译模型合并到搜索引擎的模型数据中之后，使用翻译模型可以用作根据相关性产生当前搜索查询和排序文档/广告的相关性分数的特征。

公开(公告)号：US20130103493A1

公开(公告)日：2013-04-25

申请号：US13328924

申请日：2011-12-16

申请人： Jianfeng Gao ,  Xuedong Huang ,  Mei Li ,  Zhenghao Wang ,  Christopher John Brockett ,  William B. Dolan

发明人： Jianfeng Gao ,  Xuedong Huang ,  Mei Li ,  Zhenghao Wang ,  Christopher John Brockett ,  William B. Dolan

IPC分类号： G06Q30/02 ,  G06F17/30

CPC分类号： G06Q10/10 ,  G06F17/30687 ,  G06Q30/0241

摘要： The subject disclosure is directed towards developing a translation model for mapping search query terms to document-related data. By processing user logs comprising search histories into word-aligned query-document pairs, the translation model may be trained using data, such as probabilities, corresponding to the word-aligned query-document pairs. After incorporating the translation model into model data for a search engine, the translation model is used may used as features for producing relevance scores for current search queries and ranking documents/advertisements according to relevance.

摘要翻译： 本发明旨在开发用于将搜索查询词语映射到文档相关数据的翻译模型。 通过将包括搜索历史的用户日志处理成字对齐的查询 - 文档对，可以使用对应于字对齐的查询 - 文档对的诸如概率的数据来训练翻译模型。 在将翻译模型合并到搜索引擎的模型数据中之后，使用翻译模型可以用作根据相关性产生当前搜索查询和排序文档/广告的相关性分数的特征。

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。

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。

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。

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。

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。

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。