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    • 6. 发明授权
    • Method and system for enhanced data searching
    • 增强数据搜索的方法和系统
    • US07398201B2
    • 2008-07-08
    • US10371399
    • 2003-02-19
    • Giovanni B. MarchisioKrzysztof KoperskiJisheng LiangAlejandro MuruaThien NguyenCarsten TuskNavdeep S. DhillonLubos Pochman
    • Giovanni B. MarchisioKrzysztof KoperskiJisheng LiangAlejandro MuruaThien NguyenCarsten TuskNavdeep S. DhillonLubos Pochman
    • G06F17/27G06F7/00G06F17/30
    • G06F17/271G06F17/279G06F17/30616G06F17/30684Y10S707/99933Y10S707/99943
    • Methods and systems for syntactically indexing and searching data sets to achieve more accurate search results and for indexing and searching data sets using entity tags alone or in combination therewith are provided. Example embodiments provide a Syntactic Query Engine (“SQE”) that parses, indexes, and stores a data set, as well as processes natural language queries subsequently submitted against the data set. The SQE comprises a Query Preprocessor, a Data Set Preprocessor, a Query Builder, a Data Set Indexer, an Enhanced Natural Language Parser (“ENLP”), a data set repository, and, in some embodiments, a user interface. After preprocessing the data set, the SQE parses the data set according to a variety of levels of parsing and determines as appropriate the entity tags and syntactic and grammatical roles of each term to generate enhanced data representations for each object in the data set. The SQE indexes and stores these enhanced data representations in the data set repository. Upon subsequently receiving a query, the SQE parses the query also using a variety of parsing levels and searches the indexed stored data set to locate data that contains similar terms used in similar grammatical roles and/or with similar entity tag types as indicated by the query. In this manner, the SQE is able to achieve more contextually accurate search results more frequently than using traditional search engines.
    • 提供了用于语法索引和搜索数据集以实现更精确的搜索结果以及使用单独或与其组合的实体标签索引和搜索数据集的方法和系统。 示例性实施例提供了解析,索引和存储数据集的语法查询引擎(“SQE”),并且处理随后针对数据集提交的自然语言查询。 SQE包括查询预处理器,数据集预处理器,查询生成器,数据集索引器,增强自然语言解析器(“ENLP”),数据集存储库以及在一些实施例中的用户界面。 在对数据集进行预处理之后,SQE根据各种解析级别解析数据集,并酌情确定每个术语的实体标签和句法和语法角色,以生成数据集中每个对象的增强数据表示。 SQE索引并将这些增强型数据表示存储在数据集存储库中。 在随后接收到查询时,SQE还使用各种解析级别对查询进行解析,并搜索索引存储的数据集,以定位包含类似语法角色中使用的类似术语的数据和/或与查询所指示的类似实体标记类型相关的数据 。 以这种方式,SQE能够比使用传统的搜索引擎更频繁地获得更加内容相对准确的搜索结果。
    • 10. 发明申请
    • NLP-based entity recognition and disambiguation
    • 基于NLP的实体识别和消歧
    • US20090144609A1
    • 2009-06-04
    • US12288158
    • 2008-10-15
    • Jisheng LiangKrzysztof KoperskiNavdeep S. DhillonCarsten TuskSatish Bhatti
    • Jisheng LiangKrzysztof KoperskiNavdeep S. DhillonCarsten TuskSatish Bhatti
    • G06F7/06G06F17/30G06F17/00G06F3/048
    • G06F17/21G06F17/278
    • Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.
    • 提供了使用自然语言处理技术进行实体识别和消歧的方法和系统。 示例性实施例提供了一种实体识别和消歧系统(ERDS)和过程,其基于文本段的输入,使用自然语言处理技术自动确定文本正在引用哪些实体以及从上下文数据中收集的信息的分析 周围的文字。 在至少一些实施例中,可以用于帮助识别和/或消歧过程的补充或相关信息可以从诸如本体知识库的知识库中检索。 在一个实施例中,ERDS包括语言分析引擎,知识分析引擎和消歧引擎,其协作以从知识库识别候选实体,并且使用以下方式确定哪个候选最符合文本段中的一个或多个检测到的实体 上下文信息。