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    • 1. 发明授权
    • General purpose correction of grammatical and word usage errors
    • 通用修正语法和文字使用错误
    • US09262397B2
    • 2016-02-16
    • US12961516
    • 2010-12-07
    • Michael GamonChristian König
    • Michael GamonChristian König
    • G10L15/00G10L15/19G10L15/183G06F17/20G06F17/27
    • G06F17/274G06F17/20G06F17/27G10L15/00G10L15/183G10L15/19
    • Architecture that detects and corrects writing errors in a human language based on the utilization of three different stages: error detection, correction candidate generation, and correction candidate ranking. The architecture is a generic framework for generating fluent alternatives to non-grammatical word sequences in a written sample. Error detection is addressed by a suite of language model related scores and other scores such as parse scores that can identify a particularly unlikely sequence of words. Correction candidate generation is addressed by a lookup in a very large corpus of “correct” English that looks for alternative arrangements of the same or similar words or subsequences of these words in the same context. Correction candidate ranking is addressed by a language model ranker.
    • 基于利用三个不同阶段的错误检测,校正候选者生成和校正候选排名来检测和纠正以人类语言写入错误的架构。 该架构是用于在书面样本中生成流畅的非语法词序列替代的通用框架。 错误检测通过一套语言模型相关分数和其他分数(例如可以识别特别不可能的单词序列的分析分数)来解决。 校正候选生成通过在一个非常大的“正确”英语语料库中进行查找来寻找,该语料库在相同的上下文中寻找这些单词的相同或相似单词或子序列的替代布置。 校正候选人排名由语言模型游击者处理。
    • 2. 发明申请
    • GENERAL PURPOSE CORRECTION OF GRAMMATICAL AND WORD USAGE ERRORS
    • 一般用法修正了字母和字的使用错误
    • US20120089387A1
    • 2012-04-12
    • US12961516
    • 2010-12-07
    • Michael GamonChristian König
    • Michael GamonChristian König
    • G06F17/27
    • G06F17/274G06F17/20G06F17/27G10L15/00G10L15/183G10L15/19
    • Architecture that detects and corrects writing errors in a human language based on the utilization of three different stages: error detection, correction candidate generation, and correction candidate ranking. The architecture is a generic framework for generating fluent alternatives to non-grammatical word sequences in a written sample. Error detection is addressed by a suite of language model related scores and other scores such as parse scores that can identify a particularly unlikely sequence of words. Correction candidate generation is addressed by a lookup in a very large corpus of “correct” English that looks for alternative arrangements of the same or similar words or subsequences of these words in the same context. Correction candidate ranking is addressed by a language model ranker.
    • 基于利用三个不同阶段的错误检测,校正候选者生成和校正候选排名来检测和纠正以人类语言写入错误的架构。 该架构是用于在书面样本中生成流畅的非语法词序列替代的通用框架。 错误检测通过一套语言模型相关分数和其他分数(例如可以识别特别不可能的单词序列的分析分数)来解决。 校正候选生成通过在一个非常大的“正确”英语语料库中进行查找来寻找,该语料库在相同的上下文中寻找这些单词的相同或相似单词或子序列的替代布置。 校正候选人排名由语言模型游击者处理。