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    • 1. 发明申请
    • CLICK-THROUGH PREDICTION FOR NEWS QUERIES
    • 点击通过预测新闻查询
    • US20100299350A1
    • 2010-11-25
    • US12469692
    • 2009-05-21
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • G06F17/30
    • G06F17/30864
    • Described is estimating whether an online search query is a news-related query, and if so, outputting news-related results in association with other search results returned in response to the query. The query is processed into features, including by accessing corpora that corresponds to relatively current events, e.g., recently crawled from news and blog articles. A corpus of static reference data, such as an online encyclopedia, may be used to help determine whether the query is less likely to be about current events. Features include frequency-related data and context-related data corresponding to frequency and context information maintained in the corpora. Additional features may be obtained by processing text of the query itself, e.g., “query-only” features.
    • 描述了估计在线搜索查询是否是新闻相关查询,如果是,则输出与响应于该查询返回的其他搜索结果相关联的新闻相关结果。 该查询被处理成特征,包括通过访问对应于相对当前事件的语料库,例如最近从新闻和博客文章中爬行。 可以使用诸如在线百科全书的静态参考数据的语料库来帮助确定查询是否不太可能关于当前事件。 特征包括频率相关数据和对应于语料库中维护的频率和上下文信息的上下文相关数据。 可以通过处理查询本身的文本,例如“仅查询”特征来获得附加特征。
    • 2. 发明授权
    • Click-through prediction for news queries
    • 新闻查询的点击式预测
    • US08719298B2
    • 2014-05-06
    • US12469692
    • 2009-05-21
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • Arnd Christian KonigMichael GamonQiang WuRoger P. MenezesMonwhea Jeng
    • G06F17/30
    • G06F17/30864
    • Described is estimating whether an online search query is a news-related query, and if so, outputting news-related results in association with other search results returned in response to the query. The query is processed into features, including by accessing corpora that corresponds to relatively current events, e.g., recently crawled from news and blog articles. A corpus of static reference data, such as an online encyclopedia, may be used to help determine whether the query is less likely to be about current events. Features include frequency-related data and context-related data corresponding to frequency and context information maintained in the corpora. Additional features may be obtained by processing text of the query itself, e.g., “query-only” features.
    • 描述了估计在线搜索查询是否是新闻相关查询,如果是,则输出与响应于该查询返回的其他搜索结果相关联的新闻相关结果。 该查询被处理成特征,包括通过访问对应于相对当前事件的语料库,例如最近从新闻和博客文章中爬行。 可以使用诸如在线百科全书的静态参考数据的语料库来帮助确定查询是否不太可能关于当前事件。 特征包括频率相关数据和对应于语料库中维护的频率和上下文信息的上下文相关数据。 可以通过处理查询本身的文本,例如“仅查询”特征来获得附加特征。
    • 3. 发明授权
    • Leveraging cross-document context to label entity
    • 利用跨文档上下文标签实体
    • US07970808B2
    • 2011-06-28
    • US12114824
    • 2008-05-05
    • Arnd Christian KonigVenkatesh Ganti
    • Arnd Christian KonigVenkatesh Ganti
    • G06F17/30
    • G06F17/278G06F17/2785Y10S707/962
    • 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.
    • 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。
    • 4. 发明申请
    • QUERY CLASSIFICATION USING SEARCH RESULT TAG RATIOS
    • 使用搜索结果标签比例查询分类
    • US20110125791A1
    • 2011-05-26
    • US12625594
    • 2009-11-25
    • Arnd Christian KonigVenkatesh GantiXiao Li
    • Arnd Christian KonigVenkatesh GantiXiao Li
    • G06F17/30
    • G06F16/951
    • 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).
    • 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)。
    • 8. 发明申请
    • QUERY PROGRESS ESTIMATION
    • 查询进度估计
    • US20130151504A1
    • 2013-06-13
    • US13315306
    • 2011-12-09
    • Christian KonigBolin DingSurajit ChaudhuriVivek Narasayya
    • Christian KonigBolin DingSurajit ChaudhuriVivek Narasayya
    • G06F17/30
    • G06F16/245
    • The claimed subject matter provides a method for providing a progress estimate for a database query. The method includes determining static features of a query plan for the database query. The method also includes selecting an initial progress estimator based on the static features and a trained machine learning model. The model is trained using static features of a plurality of query plans, and dynamic features of the plurality of query plans. Further, the method includes determining dynamic features of the query plan for each of a plurality of candidate estimators. Additionally, the method includes selecting a revised progress estimator based on the static features, the dynamic features and a trained machine learning model for each of the candidate estimators. The method further includes producing the progress estimate based on the revised progress estimator.
    • 所要求保护的主题提供了一种用于提供数据库查询的进度估计的方法。 该方法包括确定数据库查询的查询计划的静态特征。 该方法还包括基于静态特征和经过训练的机器学习模型来选择初始进度估计器。 使用多个查询计划的静态特征以及多个查询计划的动态特征来训练该模型。 此外,该方法包括确定多个候选估计器中的每一个的查询计划的动态特征。 另外,该方法包括基于静态特征,动态特征和针对每个候选估计器的经训练的机器学习模型来选择修正的进度估计器。 该方法还包括基于修改的进度估计器产生进度估计。