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    • 7. 发明申请
    • Systems and methods for structural indexing of natural language text
    • 自然语言文本结构索引的系统和方法
    • US20070073533A1
    • 2007-03-29
    • US11405385
    • 2006-04-17
    • Giovanni ThioneMartin Van Den Berg
    • Giovanni ThioneMartin Van Den Berg
    • G06F17/27
    • G06F17/279
    • A structural natural language index is created by segmenting documents within a repository into text portions and extracting named entity, co-reference, lexical entries, structural-semantic relationships, speaker attribution and meronymic derived features. A constituent structure is determined that contains the constituent elements and ordering information sufficient to reconstruct the text portion. A functional structure of the text portions is determined. A set of characterizing predicative triples are formed from the functional structure by applying linearization transfer rules. The constituent structure, the characterizing predicative triples and the derived features are combined to form a canonical form of the text portion. Each canonical form is added to the structural natural language index. A retrieved question is classified to determine question type and a corresponding canonical form for the question is generated. The entries in the structural natural language index are searched for entries matching the canonical form of the question and relevant to the question type. The characterizing predicative triples are used in conjunction with a generation grammar to create an answer. If the generation fails, some or all of the constituent structure of the matching entry is returned as the answer.
    • 通过将仓库内的文档分割成文本部分并提取命名实体,共同参考,词汇条目,结构语义关系,说话者归属和meronymic派生特征来创建结构自然语言索引。 确定包含足以重建文本部分的组成元素和排序信息的组成结构。 确定文本部分的功能结构。 通过应用线性化转移规则,从功能结构形成一组表征性谓词三元组。 组合结构,特征谓词三元组和派生特征被组合形成文本部分的规范形式。 每个规范形式被添加到结构自然语言指数。 检索到的问题被分类以确定问题类型,并且生成关于问题的相应的规范形式。 搜索结构自然语言索引中的条目,以匹配问题的规范形式并与问题类型相关的条目。 表征谓词三元组与一代生成语法一起使用以创建答案。 如果生成失败,则返回匹配条目的部分或全部组成结构作为答案。
    • 8. 发明申请
    • Systems and methods for collaborative note-taking
    • 用于协同笔记的系统和方法
    • US20050171926A1
    • 2005-08-04
    • US10768675
    • 2004-02-02
    • Giovanni ThioneLaurent DenoueMartin Van Den Berg
    • Giovanni ThioneLaurent DenoueMartin Van Den Berg
    • G06F3/16G06F7/00G06F17/24G10L15/00G10L15/06G10L15/18G10L15/22G10L15/24G10L15/28G10L17/00
    • G06F17/24G10L15/22G10L2015/228Y10S707/99936
    • Techniques are provided for determining collaborative notes and automatically recognizing speech, handwriting and other type of information. Domain and optional actor/speaker information associated with the support information is determined. An initial automatic speech recognition model is determined based on the domain and/or actor information. The domain and/or actor/speaker language model is used to recognize text in the speech information associated with the support information. Presentation support information such as slides, speaker notes and the like are determined. The semantic overlap between the support information and the salient non-function words in the recognized text and collaborative user feedback information are used to determine relevancy scores for the recognized text. Grammaticality, well formedness, self referential integrity and other features are used to determine correctness scores. Suggested collaborative notes are displayed in the user interface based on the salient non-function words. User actions in the user interface determine feedback signals. Recognition models such as automatic speech recognition, handwriting recognition are determined based on the feedback signals and the correctness and relevance scores.
    • 提供了用于确定协作笔记并自动识别语音,手写和其他类型的信息的技术。 确定与支持信息相关联的域和可选演员/扬声器信息。 基于域和/或行为者信息确定初始自动语音识别模型。 域和/或演员/扬声器语言模型用于识别与支持信息相关联的语音信息中的文本。 确定幻灯片,说话者笔记等的演示支援信息。 使用识别文本中的支持信息和显着非函数词之间的语义重叠以及协作用户反馈信息来确定识别文本的相关性得分。 使用语法,良好的形态,自我参照完整性等特征来确定正确性分数。 基于显着的非功能词,建议的协作笔记显示在用户界面中。 用户界面中的用户操作决定了反馈信号。 基于反馈信号和正确性和相关性分数确定识别模型,如自动语音识别,手写识别。