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1. 发明申请

US20110172988A1 ADAPTIVE CONSTRUCTION OF A STATISTICAL LANGUAGE MODEL 有权
标题翻译：统计语言模型的自适应构建
公开(公告)号：US20110172988A1
公开(公告)日：2011-07-14
申请号：US12684749
申请日：2010-01-08
申请人： Kuansan Wang , Xiaolong Li , Jiangbo Miao , Frederic H. Behr, Jr.
发明人： Kuansan Wang , Xiaolong Li , Jiangbo Miao , Frederic H. Behr, Jr.
IPC分类号： G06F17/27
CPC分类号： G06F17/2715 , G06F17/277 , G06F17/30864 , G10L15/183
摘要： A statistical language model (SLM) may be iteratively refined by considering N-gram counts in new data, and blending the information contained in the new data with the existing SLM. A first group of documents is evaluated to determine the probabilities associated with the different N-grams observed in the documents. An SLM is constructed based on these probabilities. A second group of documents is then evaluated to determine the probabilities associated with each N-gram in that second group. The existing SLM is then evaluated to determine how well it explains the probabilities in the second group of documents, and a weighting parameter is calculated from that evaluation. Using the weighting parameter, a new SLM is then constructed as a weighted average of the existing SLM and the new probabilities.
摘要翻译：可以通过考虑新数据中的N-gram计数，并将新数据中包含的信息与现有SLM进行混合来迭代地改进统计语言模型（SLM）。评估第一组文件以确定与文件中观察到的不同N-gram相关联的概率。基于这些概率构建SLM。然后评估第二组文件以确定与该第二组中的每个N-gram相关联的概率。然后评估现有SLM以确定它如何解释第二组文档中的概率，并从该评估计算加权参数。使用加权参数，然后构建新的SLM作为现有SLM的加权平均值和新概率。

2. 发明授权

US08577670B2 Adaptive construction of a statistical language model 有权
标题翻译：统计语言模型的自适应构建
公开(公告)号：US08577670B2
公开(公告)日：2013-11-05
申请号：US12684749
申请日：2010-01-08
申请人： Kuansan Wang , Xiaolong Li , Jiangbo Miao , Frederic H. Behr, Jr.
发明人： Kuansan Wang , Xiaolong Li , Jiangbo Miao , Frederic H. Behr, Jr.
IPC分类号： G06F17/27
CPC分类号： G06F17/2715 , G06F17/277 , G06F17/30864 , G10L15/183
摘要： A statistical language model (SLM) may be iteratively refined by considering N-gram counts in new data, and blending the information contained in the new data with the existing SLM. A first group of documents is evaluated to determine the probabilities associated with the different N-grams observed in the documents. An SLM is constructed based on these probabilities. A second group of documents is then evaluated to determine the probabilities associated with each N-gram in that second group. The existing SLM is then evaluated to determine how well it explains the probabilities in the second group of documents, and a weighting parameter is calculated from that evaluation. Using the weighting parameter, a new SLM is then constructed as a weighted average of the existing SLM and the new probabilities.
摘要翻译：可以通过考虑新数据中的N-gram计数，并将新数据中包含的信息与现有SLM进行混合来迭代地改进统计语言模型（SLM）。评估第一组文件以确定与文件中观察到的不同N-gram相关联的概率。基于这些概率构建SLM。然后评估第二组文件以确定与该第二组中的每个N-gram相关联的概率。然后评估现有SLM以确定它如何解释第二组文档中的概率，并从该评估计算加权参数。使用加权参数，然后构建新的SLM作为现有SLM的加权平均值和新概率。

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