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    • 1. 发明授权
    • Error-tolerant language understanding system and method
    • 容错语言理解系统和方法
    • US07333928B2
    • 2008-02-19
    • US10321492
    • 2002-12-18
    • Huei-Ming WangYi-Chung Lin
    • Huei-Ming WangYi-Chung Lin
    • G06F17/27
    • G10L15/1815
    • The present invention relates to an error-tolerant language understanding, system and method. The system and the method is using example sentences to provide the clues for detecting and recovering errors. The procedure of detection and recovery is guided by a probabilistic scoring function which integrated the scores from the speech recognizer, concept parser, the scores of concept-bigram and edit operations, such as deleting, inserting and substituting concepts. Meanwhile, the score of edit operations refers the confidence measure achieving more precise detection and recovery of the speech recognition errors. That said, a concept with lower confidence measure tends to be deleted or substituted, while a concept with higher one tends to be retained.
    • 本发明涉及一种容错语言的理解,系统和方法。 系统和方法使用例句来提供检测和恢复错误的线索。 检测和恢复过程由概率评分函数引导,该功能将来自语音识别器,概念解析器,概念二进制分数和编辑操作的分数,如删除,插入和替换概念相结合。 同时,编辑操作的得分是指实现更准确的语音识别错误检测和恢复的置信度量度。 也就是说,具有较低置信度度量的概念往往被删除或替代,而具有较高置信度的概念往往被保留。
    • 3. 发明授权
    • Method for probabilistic error-tolerant natural language understanding
    • 概率容错自然语言理解方法
    • US06920420B2
    • 2005-07-19
    • US09790947
    • 2001-02-22
    • Yi-Chung Lin
    • Yi-Chung Lin
    • G10L15/18G06F17/20
    • G10L15/1822
    • A method of probabilistic error-tolerant natural language understanding. The process of language understanding is divided into a concept parse and a concept sequence comparison steps. The concept parse uses a parse driven by a concept grammar to construct a concept parse forest set by parsing results of speech recognition. The concept sequence comparison uses an error-tolerant interpreter to compare the hypothetical concept sequences included by the concept parse forest set and the exemplary concept sequences included in the database of the system. A most possible concept sequence is found and converted into a semantic framed that expresses the intention of the user. The whole process is led by a probability oriented scoring function. When error occurs in the speech recognition and a correct concept sequence cannot be formed, the position of the error is determined and the error is recovered according to the scoring function to reduce the negative effect.
    • 一种概率容错自然语言理解的方法。 语言理解的过程分为概念分析和概念序列比较步骤。 概念解析使用由概念语法驱动的解析,通过解析语音识别结果来构建概念解析林集。 概念序列比较使用容错解释器来比较概念解析森林集合包括的假设概念序列和包括在系统数据库中的示例性概念序列。 找到最可能的概念序列并将其转换为表示用户意图的语义框架。 整个过程由面向概率的评分函数引导。 当语音识别发生错误时,无法形成正确的概念序列,确定错误的位置,并根据评分函数恢复错误,以减少负面影响。
    • 8. 发明授权
    • Method for named-entity recognition and verification
    • 命名实体识别和验证方法
    • US07171350B2
    • 2007-01-30
    • US10227470
    • 2002-08-26
    • Yi-Chung LinPeng-Hsiang Hung
    • Yi-Chung LinPeng-Hsiang Hung
    • G06F17/27
    • G06F17/278
    • A method for named-entity (NE) recognition and verification is provided. The method can extract at least one to-be-tested segments from an article according to a text window, and use a predefined grammar to parse the at least one to-be-tested segments to remove ill-formed ones. Then, a statistical verification model is used to calculate the confidence measurement of each to-be-tested segment to determine where the to-be-tested segment has a named-entity or not. If the confidence measurement is less than a predefined threshold, the to-be-tested segment will be rejected. Otherwise, it will be accepted.
    • 提供了命名实体(NE)识别和验证的方法。 该方法可以根据文本窗口从文章中提取至少一个待测试的片段,并且使用预定义的语法来解析至少一个待测试片段以去除不正确的片段。 然后,统计验证模型用于计算每个待测试段的置信度测量,以确定待测试段具有命名实体的位置。 如果置信度测量值小于预定义的阈值,则被测试段将被拒绝。 否则将被接受。
    • 10. 发明授权
    • Computer implemented example-based concept-oriented data extraction method
    • 计算机实现基于示例的概念导向数据提取方法
    • US07107524B2
    • 2006-09-12
    • US10442300
    • 2003-05-21
    • Yi-Chung LinChung-Jen Chiu
    • Yi-Chung LinChung-Jen Chiu
    • G06F17/00
    • G06F17/3071Y10S707/99936
    • The present invention relates to an example-based concept-orietned data extraction method. In an example labeling phase, the exemplary data string is converted into an exemplary token sequence, in which the target concepts and filler concepts are labeled to be tuples for use as an example, and thus an exemplary concept graph is constructed. In the data extraction phase, the untested data string is converted into an untested token sequence to be processed, and, based on the associated concept recognizers defined by the tuples in the example labeling phase, it is able to detect the concept candidates and establish the composite concepts and aggregate concepts, thereby constructing a hypothetical concept graph. After comparing the exemplary concept graph with the hypothetical concept graph, the optimal hypothetical concept sequence in the hypothetical graph is determined, so as to extract the targeted data from the matched target concepts.
    • 本发明涉及基于示例的概念设计的数据提取方法。 在示例标记阶段中,将示例性数据串转换为示例性令牌序列,其中目标概念和填充符概念被标记为用作示例的元组,因此构建示例性概念图。 在数据提取阶段,未经测试的数据串被转换成待处理的未经测试的令牌序列,并且基于在示例标签阶段中由元组定义的相关联的概念识别器,能够检测概念候选并建立 复合概念和聚合概念,从而构建一个假设的概念图。 在将示例性概念图与假设概念图进行比较之后,确定假设图中的最佳假设概念序列,以便从匹配的目标概念中提取目标数据。