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公开(公告)号：US20110125791A1
公开(公告)日：2011-05-26
申请号：US12625594
申请日：2009-11-25
申请人： Arnd Christian Konig , Venkatesh Ganti , Xiao Li
发明人： Arnd Christian Konig , Venkatesh Ganti , Xiao Li
IPC分类号： G06F17/30
CPC分类号： G06F16/951
摘要： Techniques are described herein for classifying a search query with respect to query intent using search result tag ratios. A tag is a character or a combination of characters (e.g., one or more words) that indicates a property of a document, such as a topic of the document, a type of entity (i.e., subject matter) the document references, etc. A search result tag ratio is defined as a fraction (e.g., a proportion, a percentage, etc.) of the documents in a search result that includes a respective tag. A search query may be classified based on back-off ratios, which are tag ratios of search queries that are related to the search query to be classified. Tag ratios may be pre-computed (i.e., calculated before the corresponding search queries are received from users).
摘要翻译： 这里描述了使用搜索结果标签比率来分类关于查询意图的搜索查询的技术。 标签是指示文档的属性（例如文档的主题，文档引用的实体的类型（即主题）等）的字符或字符的组合（例如，一个或多个单词）。 搜索结果标签比率被定义为包括相应标签的搜索结果中的文档的分数（例如，比例，百分比等）。 搜索查询可以基于退避比率进行分类，后退比率是与要分类的搜索查询相关的搜索查询的标签比率。 可以预先计算标签比率（即，在从用户接收相应的搜索查询之前计算）。
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公开(公告)号：US20090282012A1
公开(公告)日：2009-11-12
申请号：US12114824
申请日：2008-05-05
CPC分类号： G06F17/278 , G06F17/2785 , Y10S707/962
摘要： Entities, such as people, places and things, are labeled based on information collected across a possibly large number of documents. One or more documents are scanned to recognize the entities, and features are extracted from the context in which those entities occur in the documents. Observed entity-feature pairs are stored either in an in-memory store or an external store. A store manager optimizes use of the limited amount of space for an in-memory store by determining which store to put an entity-feature pair in, and when to evict features from the in-memory store to make room for new pairs. Feature that may be observed in an entity's context may take forms such as specific word sequences or membership in a particular list.
摘要翻译： 诸如人物，地点和事物等实体根据可能大量文件收集的信息进行标注。 扫描一个或多个文档以识别实体，并且从文档中出现这些实体的上下文提取特征。 观察到的实体特征对存储在内存存储或外部存储中。 存储管理器通过确定哪个存储放置实体特征对，以及何时从存储器内存存储器中删除特征以为新的对腾出空间来优化对存储器存储器中的有限数量的空间的使用。 可能在实体的上下文中观察到的特征可以采取诸如特定单词序列或特定列表中的成员资格的形式。
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公开(公告)号：US20090327223A1
公开(公告)日：2009-12-31
申请号：US12146469
申请日：2008-06-26
申请人： Kaushik Chakrabarti , Surajit Chaudhuri , Venkatesh Ganti , Dong Xin , Sanjay Agrawal , Arnd Christian Konig
发明人： Kaushik Chakrabarti , Surajit Chaudhuri , Venkatesh Ganti , Dong Xin , Sanjay Agrawal , Arnd Christian Konig
CPC分类号： G06F16/951
摘要： The described implementations relate to query portals. One technique analyzes search results generated by a web search engine responsive to a user search query. The technique also dynamically generates a query portal that lists the search results as well as entities identified from the search results.
摘要翻译： 所描述的实现涉及查询门户。 一种技术分析响应于用户搜索查询的web搜索引擎生成的搜索结果。 该技术还动态生成查询门户，其中列出搜索结果以及从搜索结果中识别的实体。
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公开(公告)号：US07970808B2
公开(公告)日：2011-06-28
申请号：US12114824
申请日：2008-05-05
IPC分类号： G06F17/30
CPC分类号： G06F17/278 , G06F17/2785 , Y10S707/962
摘要： Entities, such as people, places and things, are labeled based on information collected across a possibly large number of documents. One or more documents are scanned to recognize the entities, and features are extracted from the context in which those entities occur in the documents. Observed entity-feature pairs are stored either in an in-memory store or an external store. A store manager optimizes use of the limited amount of space for an in-memory store by determining which store to put an entity-feature pair in, and when to evict features from the in-memory store to make room for new pairs. Feature that may be observed in an entity's context may take forms such as specific word sequences or membership in a particular list.
摘要翻译： 诸如人物，地点和事物等实体根据可能大量文件收集的信息进行标注。 扫描一个或多个文档以识别实体，并且从文档中出现这些实体的上下文提取特征。 观察到的实体特征对存储在内存存储或外部存储中。 存储管理器通过确定哪个存储放置实体特征对，以及何时从存储器内存存储器中删除特征以为新的对腾出空间来优化对存储器存储器中的有限数量的空间的使用。 可能在实体的上下文中观察到的特征可以采取诸如特定单词序列或特定列表中的成员资格的形式。
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5.发明授权公开(公告)号：US08782061B2
公开(公告)日：2014-07-15
申请号：US12144675
申请日：2008-06-24
CPC分类号： G06F17/30011 , G06F17/278
摘要： A set of documents is filtered for entity extraction. A list of entity strings is received. A set of token sets that covers the entity strings in the list is determined. An inverted index generated on a first set of documents is queried using the set of token sets to determine a set of document identifiers for a subset of the documents in the first set. A second set of documents identified by the set of document identifiers is retrieved from the first set of documents. The second set of documents is filtered to include one or more documents of the second set that each includes a match with at least one entity string of the list of entity strings. Entity recognition may be performed on the filtered second set of documents.
摘要翻译： 过滤一组文档进行实体提取。 接收到实体字符串的列表。 确定一组涵盖列表中的实体字符串的令牌集。 使用该组令牌查询在第一组文档上生成的反向索引，以确定第一组中的文档的子集的一组文档标识符。 从第一组文档中检索由该组文档标识符标识的第二组文档。 第二组文档被过滤以包括第二组的一个或多个文档，每个文档包括与实体字符串列表的至少一个实体字符串的匹配。 可以对经过滤的第二组文件执行实体识别。
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公开(公告)号：US08533203B2
公开(公告)日：2013-09-10
申请号：US12478120
申请日：2009-06-04
申请人： Surajit Chaudhuri , Venkatesh Ganti , Dong Xin
发明人： Surajit Chaudhuri , Venkatesh Ganti , Dong Xin
CPC分类号： G06F17/2795 , G06F17/278
摘要： Identifying synonyms of entities using a collection of documents is disclosed herein. In some aspects, a document from a collection of documents may be analyzed to identify hit sequences that include one or more tokens (e.g., words, number, etc.). The hit sequences may then be used to generate discriminating token sets (DTS's) that are subsets of both the hit sequences and the entity names. The DTS's are matched with corresponding entity names, and then used to create DTS phrases by selecting adjacent text in the document that is proximate to the DTS. The DTS phrases may be analyzed to determine whether the corresponding DTS is synonyms of the entity name. In various aspects, the tokens of an associated entity name that are present in the DTS phrases are used to generate a score for the DTS. When the score at least reaches a threshold, the DTS may be designated as a synonym. A list of synonyms may be generated for each entity name.
摘要翻译： 本文公开了使用文档集合识别实体的同义词。 在一些方面，可以分析来自文档集合的文档以识别包括一个或多个令牌（例如，单词，数字等）的命中序列。 然后可以使用命中序列来生成作为命中序列和实体名称的子集的识别令牌集（DTS's）。 DTS与相应的实体名称相匹配，然后用于通过选择靠近DTS的文档中的相邻文本来创建DTS短语。 可以分析DTS短语以确定对应的DTS是否是实体名称的同义词。 在各方面，使用存在于DTS短语中的关联实体名称的令牌来产生DTS的得分。 当分数至少达到阈值时，DTS可以被指定为同义词。 可以为每个实体名称生成同义词列表。
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公开(公告)号：US08195655B2
公开(公告)日：2012-06-05
申请号：US11758024
申请日：2007-06-05
IPC分类号： G06F17/30
CPC分类号： G06F17/278 , G06F17/30864
摘要： Architecture for finding related entities for web search queries. An extraction component takes a document as input and outputs all the mentions (or occurrences) of named entities such as names of people, organizations, locations, and products in the document, as well as entity metadata. An indexing component takes a document identifier (docID) and the set of mentions of named entities and, stores and indexes the information for retrieval. A document-based search component takes a keyword query and returns the docIDs of the top documents matching with the query. A retrieval component takes a docID as input, accesses the information stored by the indexing component and returns the set of mentions of named entities in the document. This information is then passed to an entity scoring and thresholding component that computes an aggregate score of each entity and selects the entities to return to the user.
摘要翻译： 用于查找网络搜索查询的相关实体的架构。 提取组件将文档作为输入并输出所有实体的所有提及（或出现），例如文档中的人员，组织，位置和产品的名称以及实体元数据。 索引组件采用文档标识符（docID）和命名实体的提及集合，并存储和索引信息进行检索。 基于文档的搜索组件接受关键字查询，并返回与查询匹配的顶级文档的docID。 检索组件将docID作为输入，访问由索引组件存储的信息，并返回文档中命名实体的提及集。 然后将该信息传递给实体计分和阈值组件，该组件计算每个实体的聚合分数，并选择要返回给用户的实体。
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8.发明申请公开(公告)号：US20110320446A1
公开(公告)日：2011-12-29
申请号：US12823124
申请日：2010-06-25
IPC分类号： G06F17/30
CPC分类号： G06F16/90335
摘要： This patent application relates to interval-based information retrieval (IR) search techniques for efficiently and correctly answering keyword search queries. In some embodiments, a range of information-containing blocks for a search query can be identified. Each of these blocks, and thus the range, can include document identifiers that identify individual corresponding documents that contain a term found in the search query. From the range, a subrange(s) having a smaller number of blocks than the range can be selected. This can be accomplished without decompressing the blocks by partitioning the range into intervals and evaluating the intervals. The smaller number of blocks in the subranges(s) can then be decompressed and processed to identify a doc ID(s) and thus document(s) that satisfies the query.
摘要翻译： 该专利申请涉及用于有效和正确地回答关键词搜索查询的基于间隔的信息检索（IR）搜索技术。 在一些实施例中，可以识别用于搜索查询的一系列含有信息的块。 这些块中的每个以及因此的范围可以包括识别包含在搜索查询中找到的术语的各个对应文档的文档标识符。 从该范围可以选择具有比该范围少的块数量的子范围。 这可以在不通过将范围划分成间隔并且评估间隔来解压缩块的情况下实现。 然后可以解压缩和处理子范围中较小数量的块，以识别文档ID，从而识别符合查询的文档。
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公开(公告)号：US07730060B2
公开(公告)日：2010-06-01
申请号：US11423303
申请日：2006-06-09
申请人： Kaushik Chakrabarti , Venkatesh Ganti , Dong Xin
发明人： Kaushik Chakrabarti , Venkatesh Ganti , Dong Xin
IPC分类号： G06F17/30
CPC分类号： G06F17/30964
摘要： The subject disclosure pertains to a class of object finder queries that return the best target objects that match a set of given keywords. Mechanisms are provided that facilitate identification of target objects related to search objects that match a set of query keywords. Scoring mechanisms/functions are also disclosed that compute relevance scores of target objects. Further, efficient early termination techniques are provided to compute the top K target objects based on a scoring function.
摘要翻译： 主题公开涉及一类对象查找器查询，其返回与一组给定关键字匹配的最佳目标对象。 提供了有助于识别与一组查询关键字匹配的搜索对象相关的目标对象的机制。 还公开了计算目标对象的相关性分数的评分机制/功能。 此外，提供有效的提前终止技术以基于评分功能计算顶部K个目标对象。
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公开(公告)号：US20090006392A1
公开(公告)日：2009-01-01
申请号：US11769050
申请日：2007-06-27
CPC分类号： G06F17/30536
摘要： Architecture that provides a data profile computation technique which employs key profile computation and data pattern profile computation. Key profile computation in a data table includes both exact keys as well as approximate keys, and is based on key strengths. A key strength of 100% is an exact key, and any other percentage in an approximate key. The key strength is estimated based on the number of table rows that have duplicated attribute values. Only column sets that exceed a threshold value are returned. Pattern profiling identifies a small set of regular expression patterns which best describe the patterns within a given set of attribute values. Pattern profiling includes three phases: a first phases for determining token regular expressions, a second phase for determining candidate regular expressions, and a third phase for identifying the best regular expressions of the candidates that match the attribute values.
摘要翻译： 提供采用关键轮廓计算和数据模式轮廓计算的数据轮廓计算技术的架构。 数据表中的关键轮廓计算包括精密键和近似键，并且基于关键优点。 100％的关键优势是一个确切的关键，其中一个关键的任何其他百分比。 基于具有重复的属性值的表行的数量来估计关键强度。 只返回超过阈值的列集。 模式分析标识一组最佳描述一组给定属性值中的模式的正则表达式模式。 模式分析包括三个阶段：用于确定令牌正则表达式的第一阶段，用于确定候选正则表达式的第二阶段，以及用于识别与属性值匹配的候选的最佳正则表达式的第三阶段。
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