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    • 5. 发明申请
    • Coreference Resolution In An Ambiguity-Sensitive Natural Language Processing System
    • 一个歧义敏感的自然语言处理系统中的核心分辨率
    • US20090076799A1
    • 2009-03-19
    • US12200962
    • 2008-08-29
    • Richard CrouchMartin Henk Van den BergFranco SalvettiGiovanni Lorenzo ThioneDavid Ahn
    • Richard CrouchMartin Henk Van den BergFranco SalvettiGiovanni Lorenzo ThioneDavid Ahn
    • G06F17/27
    • G06F17/2765G06F17/2785
    • Technologies are described herein for coreference resolution in an ambiguity-sensitive natural language processing system. Techniques for integrating reference resolution functionality into a natural language processing system can processes documents to be indexed within an information search and retrieval system. Ambiguity awareness features, as well as ambiguity resolution functionality, can operate in coordination with coreference resolution. Annotation of coreference entities, as well as ambiguous interpretations, can be supported by in-line markup within text content or by external entity maps. Information expressed within documents can be formally organized in terms of facts, or relationships between entities in the text. Expansion can support applying multiple aliases, or ambiguities, to an entity being indexed so that all of the possibly references or interpretations for that entity are captured into the index. Alternative stored descriptions can support retrieval of a fact by either the original description or a coreferential description.
    • 本文描述了在歧义敏感的自然语言处理系统中的技术。 将参考分辨率功能集成到自然语言处理系统中的技术可以处理在信息搜索和检索系统内索引的文档。 模糊识别功能以及模糊度解析功能可与协作解决方案协调工作。 可以通过文本内容或外部实体映射中的在线标记来支持对关键实体的注释以及模糊的解释。 在文件中表达的信息可以根据事实或文本中实体之间的关系正式组织。 扩展可以支持对被索引的实体应用多个别名或歧义,以便将该实体的所有可能的引用或解释都捕获到索引中。 替代存储的描述可以支持通过原始描述或者核心概念来检索事实。
    • 6. 发明授权
    • Entity category extraction for an entity that is the subject of pre-labeled data
    • 作为预标记数据主体的实体的实体类别提取
    • US09268878B2
    • 2016-02-23
    • US12820349
    • 2010-06-22
    • Michael BieniosekFranco SalvettiGiovanni Lorenzo Thione
    • Michael BieniosekFranco SalvettiGiovanni Lorenzo Thione
    • G06F17/30
    • G06F17/30943G06F17/30707
    • Summaries of entities (e.g., people, places, things, concepts, etc.) may provide additional useful information to user. For example, a search engine may provide a summary of an entity within search results. A category (e.g., “writer”, “politician”, etc.) of the entity that is short and concise may be advantageous to provide within a summary of the entity. The category may allow a user to quickly determine whether the information of the entity relates to the intended entity (e.g., search results of an entity as “a writer” vs. search results of an entity as “a politician”). Potential categories and summary text may be extracted from pre-labeled data. The potential categories and summary text may be intersected to determine a set of candidate categories that may be ranked. An entity category having a desired ranked may be determined as the entity category that describes the entity in a desired way.
    • 实体(例如,人员,地点,事物,概念等)的摘要可以向用户提供额外的有用信息。 例如,搜索引擎可以提供搜索结果内的实体的摘要。 实体的类别(例如“作者”,“政治家”等)简明扼要可能有利于在实体的总结中提供。 该类别可以允许用户快速确定实体的信息是否与预期实体相关(例如,实体作为“作者”的搜索结果与实体的搜索结果相对于“政治家”)。 可能从预先标记的数据中提取潜在的类别和摘要文本。 潜在的类别和摘要文本可以相交,以确定可能被排名的一组候选类别。 可以将具有期望排名的实体类别确定为以期望的方式描述实体的实体类别。
    • 7. 发明授权
    • Coreference resolution in an ambiguity-sensitive natural language processing system
    • 歧义敏感自然语言处理系统中的核心分辨率
    • US08712758B2
    • 2014-04-29
    • US12200962
    • 2008-08-29
    • Richard CrouchMartin Henk Van den BergFranco SalvettiGiovanni Lorenzo ThioneDavid Ahn
    • Richard CrouchMartin Henk Van den BergFranco SalvettiGiovanni Lorenzo ThioneDavid Ahn
    • G06F17/20G06F17/27
    • G06F17/2765G06F17/2785
    • Technologies are described herein for coreference resolution in an ambiguity-sensitive natural language processing system. Techniques for integrating reference resolution functionality into a natural language processing system can processes documents to be indexed within an information search and retrieval system. Ambiguity awareness features, as well as ambiguity resolution functionality, can operate in coordination with coreference resolution. Annotation of coreference entities, as well as ambiguous interpretations, can be supported by in-line markup within text content or by external entity maps. Information expressed within documents can be formally organized in terms of facts, or relationships between entities in the text. Expansion can support applying multiple aliases, or ambiguities, to an entity being indexed so that all of the possibly references or interpretations for that entity are captured into the index. Alternative stored descriptions can support retrieval of a fact by either the original description or a coreferential description.
    • 本文描述了在歧义敏感的自然语言处理系统中的技术。 将参考分辨率功能集成到自然语言处理系统中的技术可以处理在信息搜索和检索系统内索引的文档。 模糊识别功能以及模糊度解析功能可与协作解决方案协调工作。 可以通过文本内容或外部实体映射中的在线标记来支持对关键实体的注释以及模糊的解释。 在文件中表达的信息可以根据事实或文本中实体之间的关系正式组织。 扩展可以支持对被索引的实体应用多个别名或歧义,以便将该实体的所有可能的引用或解释都捕获到索引中。 替代存储的描述可以支持通过原始描述或者核心概念来检索事实。
    • 8. 发明申请
    • ENTITY CATEGORY DETERMINATION
    • 实体类别确定
    • US20110314018A1
    • 2011-12-22
    • US12820349
    • 2010-06-22
    • Michael BieniosekFranco SalvettiGiovanni Lorenzo Thione
    • Michael BieniosekFranco SalvettiGiovanni Lorenzo Thione
    • G06F17/30
    • G06F17/30943G06F17/30707
    • Summaries of entities (e.g., people, places, things, concepts, etc.) may provide additional useful information to user. For example, a search engine may provide a summary of an entity within search results. A category (e.g., “writer”, “politician”, etc.) of the entity that is short and concise may be advantageous to provide within a summary of the entity. The category may allow a user to quickly determine whether the information of the entity relates to the intended entity (e.g., search results of an entity as “a writer” vs. search results of an entity as “a politician”). Potential categories and summary text may be extracted from pre-labeled data. The potential categories and summary text may be intersected to determine a set of candidate categories that may be ranked. An entity category having a desired ranked may be determined as the entity category that describes the entity in a desired way.
    • 实体(例如,人员,地点,事物,概念等)的摘要可以向用户提供额外的有用信息。 例如,搜索引擎可以提供搜索结果内的实体的摘要。 实体的类别(例如“作者”,“政治家”等)简明扼要可能有利于在实体的总结中提供。 该类别可以允许用户快速确定实体的信息是否与预期实体相关(例如,实体作为“作者”的搜索结果与实体的搜索结果相对于“政治家”)。 可能从预先标记的数据中提取潜在的类别和摘要文本。 潜在的类别和摘要文本可以相交,以确定可能被排名的一组候选类别。 可以将具有期望排名的实体类别确定为以期望的方式描述实体的实体类别。
    • 10. 发明授权
    • Systems and methods for brokering services
    • 经纪服务的系统和方法
    • US07689645B2
    • 2010-03-30
    • US11090824
    • 2005-03-24
    • Giovanni Lorenzo ThioneMartin Henk Van Den Berg
    • Giovanni Lorenzo ThioneMartin Henk Van Den Berg
    • G06F15/16
    • G06F17/3089H04L12/66
    • Techniques are provided to determine service data features from an archive of web service transactions. Data features for functionally identical classes of service are determined. Differentiating data feature patterns uniquely identifying each service within the class are learned using machine learning, clustering, statistical analysis and the like. A service map associating services with the differentiating patterns is determined. The service map contains data feature patterns that differentiate among otherwise functionally identical services. The data features are optionally associated with past usage, objective and subjective service quality measurements and the like. The data features of the received service requests are compared to differentiating patterns in the service map. The service associated with the differentiating patterns matching the data features of the service request is selected. The data features of the service request may include, but document language, document genre, number of words or characters, type of images, subject matter of images and the like.
    • 提供技术来从Web服务事务的归档中确定服务数据特征。 确定功能相同的服务类别的数据特征。 使用机器学习,聚类,统计分析等方法,对使用该类别中唯一标识每个服务的数据特征模式进行区分。 确定将服务与区分模式相关联的服务地图。 服务地图包含区分功能相同服务的数据特征模式。 数据特征可选地与过去的使用,客观和主观的服务质量测量等相关联。 将接收的服务请求的数据特征与服务映射中的区分模式进行比较。 选择与匹配服务请求的数据特征的区分模式相关联的服务。 服务请求的数据特征可以包括但是文档语言,文档类型,单词或字符的数量,图像的类型,图像的主题等等。